for Journals by Title or ISSN
for Articles by Keywords
help

Publisher: Elsevier   (Total: 3161 journals)

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

        1 2 3 4 5 6 7 8 | Last   [Sort by number of followers]   [Restore default list]

Showing 1 - 200 of 3161 Journals sorted alphabetically
A Practical Logic of Cognitive Systems     Full-text available via subscription   (Followers: 9)
AASRI Procedia     Open Access   (Followers: 15)
Academic Pediatrics     Hybrid Journal   (Followers: 33, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 23, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 94, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 25, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 34, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 5)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 7)
Acta Astronautica     Hybrid Journal   (Followers: 411, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 27, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 2)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 10, SJR: 0.18, CiteScore: 1)
Acta Haematologica Polonica     Free   (Followers: 1, SJR: 0.128, CiteScore: 0)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 249, SJR: 3.263, CiteScore: 6)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.504, CiteScore: 1)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.542, CiteScore: 1)
Acta Oecologica     Hybrid Journal   (Followers: 12, SJR: 0.834, CiteScore: 2)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 2, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 1, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
Acta Psychologica     Hybrid Journal   (Followers: 27, SJR: 1.331, CiteScore: 2)
Acta Sociológica     Open Access   (Followers: 1)
Acta Tropica     Hybrid Journal   (Followers: 6, SJR: 1.052, CiteScore: 2)
Acta Urológica Portuguesa     Open Access  
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 3, SJR: 0.374, CiteScore: 1)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 2)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.344, CiteScore: 1)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 1)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 6, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 6)
Acute Pain     Full-text available via subscription   (Followers: 14, SJR: 2.671, CiteScore: 5)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.53, CiteScore: 4)
Addictive Behaviors     Hybrid Journal   (Followers: 16, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 8, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 9, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 22)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 147, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 11, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 16, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 8, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 12, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 28, SJR: 0.126, CiteScore: 0)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 2)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 10, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 11, SJR: 1.551, CiteScore: 4)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 22, SJR: 2.089, CiteScore: 5)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 14, SJR: 0.572, CiteScore: 2)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.61, CiteScore: 7)
Advances in Botanical Research     Full-text available via subscription   (Followers: 2, SJR: 0.686, CiteScore: 2)
Advances in Cancer Research     Full-text available via subscription   (Followers: 31, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 8, SJR: 1.453, CiteScore: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 1.992, CiteScore: 5)
Advances in Cell Aging and Gerontology     Full-text available via subscription   (Followers: 3)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 12)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 27, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 10, SJR: 1.316, CiteScore: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 29, SJR: 1.562, CiteScore: 3)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 19, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 11)
Advances in Digestive Medicine     Open Access   (Followers: 9)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 5)
Advances in Drug Research     Full-text available via subscription   (Followers: 24)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 28, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 7)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 44, SJR: 5.39, CiteScore: 8)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 56, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
Advances in Genetics     Full-text available via subscription   (Followers: 16, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 8, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 6, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 21, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 12, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 36, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 1.163, CiteScore: 2)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.938, CiteScore: 3)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 6, SJR: 0.176, CiteScore: 0)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.682, CiteScore: 2)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Marine Biology     Full-text available via subscription   (Followers: 16, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 11, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 6, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 5)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 4, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 21)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.182, CiteScore: 0)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 3)
Advances in Oncobiology     Full-text available via subscription   (Followers: 1)
Advances in Organ Biology     Full-text available via subscription   (Followers: 1)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 17, SJR: 1.875, CiteScore: 4)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.174, CiteScore: 0)
Advances in Parasitology     Full-text available via subscription   (Followers: 5, SJR: 1.579, CiteScore: 4)
Advances in Pediatrics     Full-text available via subscription   (Followers: 24, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 10)
Advances in Pharmacology     Full-text available via subscription   (Followers: 16, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 0.574, CiteScore: 1)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.109, CiteScore: 1)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 9)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 5)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 18)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 20, SJR: 0.791, CiteScore: 2)
Advances in Psychology     Full-text available via subscription   (Followers: 62)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (SJR: 0.263, CiteScore: 1)
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 3, SJR: 0.101, CiteScore: 0)
Advances in Space Biology and Medicine     Full-text available via subscription   (Followers: 5)
Advances in Space Research     Full-text available via subscription   (Followers: 397, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 5)
Advances in Surgery     Full-text available via subscription   (Followers: 10, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 31, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 17)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 13)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 47, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 341, SJR: 0.796, CiteScore: 3)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.42, CiteScore: 2)
African J. of Emergency Medicine     Open Access   (Followers: 6, SJR: 0.296, CiteScore: 0)
Ageing Research Reviews     Hybrid Journal   (Followers: 11, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 446, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 17, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 32, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 44, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 2)
Agriculture and Natural Resources     Open Access   (Followers: 3)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 57, SJR: 1.747, CiteScore: 4)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.589, CiteScore: 3)
Air Medical J.     Hybrid Journal   (Followers: 6, SJR: 0.26, CiteScore: 0)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.19, CiteScore: 0)
Alcohol     Hybrid Journal   (Followers: 11, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 9)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 1, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 11, SJR: 1.142, CiteScore: 4)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 2)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.504, CiteScore: 1)
Allergology Intl.     Open Access   (Followers: 5, SJR: 1.148, CiteScore: 2)
Alpha Omegan     Full-text available via subscription   (SJR: 3.521, CiteScore: 6)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 9, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 50, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 4, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 4, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 50, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 54, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 45, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 10)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 34, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 28, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 34, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 46)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3, SJR: 1.967, CiteScore: 2)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 205, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 62, SJR: 3.184, CiteScore: 4)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 5, SJR: 0.265, CiteScore: 0)
American J. of Orthodontics and Dentofacial Orthopedics     Full-text available via subscription   (Followers: 6, SJR: 1.289, CiteScore: 1)
American J. of Otolaryngology     Hybrid Journal   (Followers: 25, SJR: 0.59, CiteScore: 1)
American J. of Pathology     Hybrid Journal   (Followers: 27, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 28, SJR: 2.164, CiteScore: 4)
American J. of Surgery     Hybrid Journal   (Followers: 38, SJR: 1.141, CiteScore: 2)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 12, SJR: 0.767, CiteScore: 1)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 6)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.144, CiteScore: 3)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 62, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 17, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription  
Anales de Pediatría     Full-text available via subscription   (Followers: 3, SJR: 0.277, CiteScore: 0)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription  
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 5, SJR: 4.849, CiteScore: 10)
Analytica Chimica Acta     Hybrid Journal   (Followers: 43, SJR: 1.512, CiteScore: 5)
Analytical Biochemistry     Hybrid Journal   (Followers: 177, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 11, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 11)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 23, SJR: 0.683, CiteScore: 2)
Angiología     Full-text available via subscription   (SJR: 0.121, CiteScore: 0)
Angiologia e Cirurgia Vascular     Open Access   (Followers: 1, SJR: 0.111, CiteScore: 0)
Animal Behaviour     Hybrid Journal   (Followers: 189, SJR: 1.58, CiteScore: 3)

        1 2 3 4 5 6 7 8 | Last   [Sort by number of followers]   [Restore default list]

Journal Cover
Neurocomputing
Journal Prestige (SJR): 1.073
Citation Impact (citeScore): 4
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0925-2312
Published by Elsevier Homepage  [3161 journals]
  • Convergence of decomposition methods for support vector machines
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Qiaozhi Zhang, Di Wang, Yanguo WangAbstractDecomposition methods play an important role in solving large-scale quadratic programming (QP) problems arising from support vector machines (SVMs). In this paper, we study convergence of general decomposition methods for SVMs. We prove that, under a mild condition on the working set selection, a decomposition algorithm stops within a finite number of iterations after reaching a solution of the QP problem satisfying a relaxed Karush–Kuhn–Tucker (KKT) condition which has been often used so far. Further, it is shown that the working set selection used in the implementation of SVMlight satisfies the condition given in this paper, so SVMlight has the finite termination property without the stronger assumption than the positive-semi-definiteness on the Hessian matrix of the objection function.
       
  • Forecasting neural network model with novel CID learning rate and EEMD
           algorithms on energy market
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Zhongpei Cen, Jun WangAbstractIn view of the applications of artificial neural networks in economic and financial forecasting, it is vital to improve the prediction methods and the forecasting accuracy for the neural networks. In this paper, a neural network architecture with novel learning rate which is controlled by the complexity invariant distance (CID) is developed for energy market forecasting, where the CID is generally utilized to measure the complexity differences between two time series by employing the Euclidean distance. Moreover, stochastic time strength neural network (STNN) is a kind of supervised neural network which is introduced to forecast the time series. Based on the above theories, a new neural network model called CID-STNN is proposed in this work, in an attempt to improve the forecasting accuracy. For comparing the forecasting performance of CID-STNN and STNN deeply, the ensemble empirical mode decomposition (EEMD) is applied to decompose time series into several intrinsic mode functions (IMFs), and these IMFs are utilized to train the models. Further, the empirical research is performed in testing the prediction effect of WTI and Brent by evaluating predicting ability of the proposed model, and the corresponding superiority is also demonstrated.
       
  • Sequence recognition of Chinese license plates
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Jianlin Wang, He Huang, Xusheng Qian, Jinde Cao, Yakang DaiAbstractThe recognition of license plates is very important for intelligent transportation systems. Generally, the performance of an intelligent recognition algorithm is greatly affected by different shooting angles, illumination conditions and backgrounds of the license plate images. This paper presents a sequence recognition approach for intelligent recognition of Chinese license plates. Firstly, a spatial transformer network (STN) is employed to adjust the inclined and deformed license plates such that all the plates have a uniform orientation and thus are easier to be recognized. Then, an improved convolutional neural network (CNN) is designed to extract sequence features of the rectified license plates. The features of different convolutional layers are integrated as input to a bi-directional recurrent neural network (BRNN), where the character segmentation is not needed. Finally, the recognition is accomplished by the BRNN and connectionist temporal classification (CTC). Due to the lack of adequate Chinese license plates, an effective training method is presented in which the network is pre-trained by sufficiently enough synthetic license plates and is fine-tuned by our collected real Chinese license plates. The experimental results show that our model achieves better recognition accuracy and lower average edit distance than some existing methods.
       
  • Robust long-term correlation tracking using convolutional features and
           detection proposals
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Bin Lin, Ying Li, Xizhe Xue, Jonathan Cheung-Wai ChanAbstractCorrelation filter based trackers have achieved appealing performance and high efficiency in recent years. However, for long-term tracking where target objects undergo dramatic appearance variation due to heavy occlusion or out-of-view, conventional correlation filter based tracking algorithms would be distracted by irrelevant objects. Once the trained tracker loses its way, it is impossible to recover the information for the following frames as the model has drifted. In this paper, we decompose the long-term tracking task into tracking and detection. Tracker learns separate correlation filters for explicit translation and scale estimation. Specifically, in order to improve tracking accuracy, the convolutional features for translation filter are extracted, and the scale filter is learned using the target appearance sampled at different scales. Detector trains an online long-term filter and applies it to the entire frame to generate detection proposals. By exploiting these detection proposals, it helps the tracker to recover from problems such as temporary or persistent occlusions. In this way, the proposed approach could handle the model drifting problem effectively for long-term tracking with more accurate estimation of object scale and location. Extensive experimental results on large-scale benchmark sequences have shown the robustness of the proposed method.
       
  • A new method for global stability analysis of delayed
           reaction–diffusion neural networks
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Xiaomei Lu, Wu-Hua Chen, Zhen Ruan, Tingwen HuangAbstractThis paper presents improved criteria for global exponential stability of reaction–diffusion neural networks with time-varying delays. A novel diffusion-dependent Lyapunov functional, which is directly linked to the diffusion terms, is suggested to analyze the role of diffusivity of each neuron on the model dynamics. In the case of Dirichlet boundary conditions, the extended Wirtinger’s inequality is employed to exploit the stabilizing effect of reaction–diffusion terms. In the framework of descriptor system approach, the augmented Lyapunov functional technique is utilized to reduce the conservatism in the values of the time delay bounds. As a result, the derived global stability criteria are more effective than the existing ones. Three numerical examples are provided to illustrate the effectiveness of the proposed methodology.
       
  • Further synchronization in finite time analysis for time-varying delayed
           fractional order memristive competitive neural networks with leakage delay
           
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): A. Pratap, R. Raja, Jinde Cao, G. Rajchakit, Fuad E. AlsaadiAbstractThis article mainly concerns the synchronization in finite-time to the time-varying delay fractional order memristive competitive neural networks (TMFCNNs) with leakage delay. By means of Fillipov’s theory, Gronwall–Bellman integral inequality, Ho¨lder’s inequality, and the Caputo derivative properties, the novel algebraic sufficient conditions are proposed to guarantee the synchronization in finite time of addressing TMFCNNs with non-integer order: 0
       
  • Rank pooling dynamic network: Learning end-to-end dynamic characteristic
           for action recognition
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Zhigang Zhu, Hongbing Ji, Wenbo Zhang, Yiping XuAbstractIn video recognition, rank-pooling operators are a type of models for sorting video sequences, which act on either the raw inputs or the intermediate feature maps of convolutional neural network (CNN). However, such models are currently restricted in the optimization of the linear ranking function by Rank-SVM and Rank-SVR. In this paper, we first propose a CNN architecture called RGB Rank Pooling Dynamic Network (RGB-RPDN), mapping a video to multiple frame-level dynamic spaces with the same size as the input. Importantly, a classical classification (e.g. FC, CNN) advanced in 2D image can be jointly positioned behind the generated representation for action classification, thus the joint architecture can be trained in an end-to-end manner. Second, we analyze how the flow-level evolution can be modeled by the hand-crafted rank-pooling machine, and extend the dynamic space of frame-level to that of flow-level by the Flow Rank Pooling Dynamic Network (Flow-RPDN). Third, equivalence relations between hand-crafted rank-pooling and RPDN are formulated, further the comparison of computing cost are qualitatively analyzed. Finally, the frame-level and flow-level pipelines are combined to achieve the final prediction by the late fusion. Specifically, with the models pre-trained on the large-scale Kinetics dataset, we train the two-stream RPDN on the UCF101 and HMDB51, where the parameters are initialized by the pre-trained models above. Experimental results demonstrate that the RPDN significantly improves the hand-crafted rank-pooling machines by a large margin of promotion, and achieves the correct rate of more excellent classification in action recognition.
       
  • A multiobjective optimization-based sparse extreme learning machine
           algorithm
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Yu Wu, Yongshan Zhang, Xiaobo Liu, Zhihua Cai, Yaoming CaiAbstractExtreme Learning Machine (ELM) is a popular machine learning method and has been widely applied to real-world problems due to its fast training speed and good generalization performance. However, in ELM, the randomly assigned input weights and hidden biases usually degrade the generalization performance. Furthermore, ELM is considered as an empirical risk minimization model and easily leads to overfitting when dataset exists some outliers. In this paper, we proposed a novel algorithm named Multiobjective Optimization-based Sparse Extreme Learning Machine (MO-SELM), where parameter optimization and structure learning are integrated into the learning process to simultaneously enhance the generalization performance and alleviate the overfitting problem. In MO-SELM, the training error and the connecting sparsity are taken as two conflicting objectives of the multiobjective model, which aims to find sparse connecting structures with optimal weights and biases. Then, a hybrid encoding-based MOEA/D is used to optimize the multiobjective model. In addition, ensemble learning is embedded into this algorithm to make decisions after multiobjective optimization. Experimental results of several classification and regression applications demonstrate the effectiveness of the proposed MO-SELM.
       
  • Adaptive algorithms for computing the principal Takagi vector of a complex
           symmetric matrix
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Maolin Che, Sanzheng Qiao, Yimin WeiAbstractIn this paper, we present a unified framework for deriving and analyzing adaptive algorithms for computing the principal Takagi vector of a complex symmetric matrix. Eight systems of complex-valued ordinary differential equations (complex-valued ODEs) are derived and their convergence behavior is analyzed. We prove that the solutions of the complex-valued ODEs are asymptotically stable. The systems can be implemented on neural networks. Finally, we show experimental results to support our analyses.
       
  • A nonlinear and noise-tolerant ZNN model solving for time-varying linear
           matrix equation
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Xiaoxiao Li, Jiguo Yu, Shuai Li, Lina NiAbstractThe Zhang neural network (ZNN) has attracted a great deal of interest from a large number of researchers because of its significant advantage in solving the various time-varying problems by the monotonously increasing odd activation functions. Many related models have been proposed for time-varying matrix solutions, however, provided that the noise is zero or the preprocessing of de-noising is conducted. Therefore, many of the models previously proposed are not suitable for real-world situations. In this study, a nonlinear and noise-tolerant ZNN model, named NNT-ZNN, is proposed and discussed based on the matrix-valued error function. Theoretically, we prove that the proposed NNT-ZNN model can be globally converged to the theory solution of the considered time-varying equation, regardless of any activation function being applied. In addition, we prove that the resultant NNT-ZNN model has the superior convergence performance beside the existing ZNN models, even when noise is not zero. After that, the simulative results of the resultant NNT-ZNN model are provided by using three illustrative examples to thoroughly validate the correctness of the theoretical analysis. Moreover, the simulation comparison between the proposed NNT-ZNN model and the existing ZNN-1 model is conducted, which further show that availability and excellence of the resultant NNT-ZNN model, and robustness to noise.
       
  • A discriminative feature set in the fast phase of spikes for sorting
           oligo-unit discharges of arterial baroreceptors
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Haixia Huang, Haiyan Zhao, Sitao Zhang, Ping Liu, Jie Ren, Xinling Geng, Hua Wei, Weizhen Niu, Wei WangAbstractThe present study was aimed to establish a simple and robust protocol that was more suitable for sorting discharges of the arterial baroreceptor. Oligo-unit (≤ 5) baroreceptor discharges were recorded in vitro from fine filaments of the rabbit carotid sinus nerve. Different time windows, covering the fast phase only or both the fast and slow phases of the spike were used to extract spike event data for sorting. Three measurements focusing on the fast phase of spikes—the maximum slope in the ascending limb from the half amplitude to the peak, the peak amplitude, and the width of the spike at the half amplitude—were selected as a feature set. The performance of this measurement-based analysis with subsequent K-means algorithm (MBAKM) in sorting oligo-unit discharges was compared with the performance of principal component analysis followed by K-means (PCAKM) and template matching (TM). The present study proved that: (1) MBAKM was more discriminative with less intervention than PCAKM and TM in determining the number of clusters and cluster attributions of spikes; (2) there was a higher consistency (larger intersection set) among the three algorithms with narrow windows of 0.45–0.65 ms than with 1.45 ms window. This study suggested that discriminative features were embodied in the fast phase of spikes and the oligo-unit discharges of baroreceptors could be sorted more robustly and accurately with less intervention by MBAKM than by PCAKM and TM. MBAKM with narrow time window would be promising in further studying baroreceptors and multiunit discharges from other neural structures.
       
  • Spatio-temporal convolutional features with nested LSTM for facial
           expression recognition
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Zhenbo Yu, Guangcan Liu, Qingshan Liu, Jiankang DengAbstractIn this paper, we propose a novel end-to-end architecture termed Spatio-Temporal Convolutional features with Nested LSTM (STC-NLSTM), which learns the muti-level appearance features and temporal dynamics of facial expressions in a joint fashion. More precisely, 3DCNN is used to extract spatio-temporal convolutional features from the image sequences that represent facial expressions, and the dynamics of expressions are modeled by Nested LSTM, which is actually coupled by two sub-LSTMs, saying T-LSTM and C-LSTM. Namely, T-LSTM is used to model the temporal dynamics of the spatio-temporal features in each convolutional layer, and C-LSTM is adopted to integrate the outputs of all T-LSTMs together so as to encode the multi-level features encoded in the intermediate layers of the network. We conduct experiments on four benchmark databases, CK+, Oulu-CASIA, MMI and BP4D, and the results show that the proposed method achieves a performance superior to the state-of-the-art methods.
       
  • Group feature selection with multiclass support vector machine
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Fengzhen Tang, Lukáš Adam, Bailu SiAbstractFeature reduction is nowadays an important topic in machine learning as it reduces the complexity of the final model and makes it easier to interpret. In some applications, the features arise from multiple sources and it is not so important to select the individual features as to select the important sources. This leads to a group feature selection problem. In this paper, we consider the group feature selection in the multiclass classification setting based on the framework of support vector machines. We reformulate it as a sparse problem by prescribing the maximum number of active groups and propose a novel method based on the ADMM algorithm. We proposed the method in such a way that the main computational load is performed in the first iteration and the remaining iterations can be computed fast. This allows us to handle large problems. We demonstrate the good performance of our method on several real-world datasets.
       
  • A unified deep artificial neural network approach to partial differential
           equations in complex geometries
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Jens Berg, Kaj NyströmAbstractIn this paper, we use deep feedforward artificial neural networks to approximate solutions to partial differential equations in complex geometries. We show how to modify the backpropagation algorithm to compute the partial derivatives of the network output with respect to the space variables which is needed to approximate the differential operator. The method is based on an ansatz for the solution which requires nothing but feedforward neural networks and an unconstrained gradient based optimization method such as gradient descent or a quasi-Newton method.We show an example where classical mesh based methods cannot be used and neural networks can be seen as an attractive alternative. Finally, we highlight the benefits of deep compared to shallow neural networks and device some other convergence enhancing techniques.
       
  • Learning solutions to two dimensional electromagnetic equations using
           LS-SVM
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Xiaoming Han, Jinjun Wang, Ziku Wu, Guofeng Li, Yan Wu, Juan LiAbstractIn this paper, a new approach based on least squares support vector machines (LS-SVM) is proposed for solving the electromagnetic equations. Firstly, the cubic spline function is employed to smooth the discontinuous boundary. LS-SVM is used to solve the modified problem. Secondly, nonlinear electromagnetic equation is solved by LS-SVM. Finally, multimedia electromagnetic equation is solved by LS-SVM. Same as to the artificial neural networks (ANN), the approximate solutions are composed of two parts. The first part is a known function that satisfies the boundary conditions. The second part is the product of two terms. One term is also a known function which vanished on the boundary. The left part is the combination of kernel functions containing regression parameters. The parameters can be obtained by solving a system of equations. The numerical results show that the proposed method in this paper is feasible.
       
  • Top k probabilistic skyline queries on uncertain data
    • Abstract: Publication date: 23 November 2018Source: Neurocomputing, Volume 317Author(s): Zhibang Yang, Kenli Li, Xu Zhou, Jing Mei, Yunjun GaoAbstractUncertainty of data is inherent in many applications, and query processing over uncertain data has gained widespread attention. The probabilistic skyline query is a powerful tool for managing uncertain data. However, the famous probabilistic skyline query, called p-skyline query, is likely to return unattractive objects which have no advantage in either their attributes or skyline probabilities with comparing to other query results. Moreover, it may return too many objects to offer any meaningful insight for customers. In this paper, we first propose a modified p-skyline (MPS) query based on a strong dominance operator to identify truly attractive results. Then we formulate a top k MPS (TkMPS) query on the basis of a new ranking criterion. We present effective approaches for processing the MPS query, and extend these approaches to process the TkMPS query. To improve the query performance, the reuse technique is adopted. Extensive experiments verify that the proposed algorithms for the MPS and TkMPS queries are efficient and effective, our MPS query can filter out 34.44% unattractive objects from the p-skyline query results at most, and although in some cases the results of the MPS and the p-skyline queries are just the same, our MPS query needs much less CPU, I/O, and memory costs.
       
  • Balance Gate Controlled Deep Neural Network
    • Abstract: Publication date: Available online 14 September 2018Source: NeurocomputingAuthor(s): Anji Liu, Yuanjun LailiBy mimicking the information expression ways in human's brain, deep neural network refers to a deeply structured framework with multiple processing layers that attempt to model high-level abstractions of the data. It is becoming a mainstream technology for pattern recognition, data mining and intelligent control at industrial scale. Previous work on constructing very deep networks, such as convolutional neural network and recurrent neural network, makes complex tasks such as image classification feasible. However, they are very limited when dealing with irregular data whose features are unstructured or even unknown. The traditional fully connected neural network is contrarily too shallow to extract high-level features. In this paper, we present a balance gate controlled deep network structure to deepen the fully connected neural network. It uses a new gating strategy to control information flow and increase network stability. Experimental results on both irregular regression and time-series forecasting demonstrate that the proposed network out-performs other ad-hoc models and is easier to train in a deeper form than the fully connected neural network.
       
  • An overview on probability undirected graphs and their applications in
           image processing
    • Abstract: Publication date: Available online 13 September 2018Source: NeurocomputingAuthor(s): Jian Zhang, Shifei Ding, Nan ZhangAbstractThis review aims to report recent developments about deep learning algorithms based on Restricted Boltzmann Machines (RBMs) and Conditional Random Fields (CRFs). Firstly, we give an overview of the general RBMs and CRFs, which are powerful methods for representing dependency of input data, and they can be treated as the basic blocks of deep neural nets as well. Secondly, this review introduces RBM variants and the deep learning models. Apart from the Deep Belief Networks (DBNs) and the Deep Boltzmann Machines (DBMs), the RBMs can be combined with the Convolutional Neural Nets (CNNs), which perform well in image recognition and image reconstruction. Thirdly, this review discusses CRFs and their applications in image annotation and scene recognition. Lastly, this review describes the developments and existing problems in neural nets and lists some experiments.
       
  • AlphaMEX:A Smarter Global Pooling Method for Convolutional Neural Networks
    • Abstract: Publication date: Available online 13 September 2018Source: NeurocomputingAuthor(s): Boxue Zhang, Qi Zhao, Wenquan Feng, Shuchang LyuAbstractDeep convolutional neural networks have achieved great success on image classification. A series of feature extractors learned from CNN have been used in many computer vision tasks. Global pooling layer plays a very important role in deep convolutional neural networks. It is found that the input feature-maps of global pooling become sparse, as the increasing use of Batch Normalization and ReLU layer combination, which makes the original global pooling low efficiency. In this paper, we proposed a novel end-to-end trainable global pooling operator AlphaMEX Global Pool for convolutional neural network. A nonlinear smooth log-mean-exp function is designed, called AlphaMEX, to extract features effectively and make networks smarter. Compared to the original global pooling layer, our proposed method can improve classification accuracy without increasing any layers or too much redundant parameters. Experimental results on CIFAR-10/CIFAR100, SVHN and ImageNet demonstrate the effectiveness of the proposed method. The AlphaMEX-ResNet outperforms original ResNet-110 by 8.3% on CIFAR10+, and the top-1 error rate of AlphaMEX-DenseNet(k=12) reaches 5.03% which outperforms original DenseNet(k=12) by 4.0%.
       
  • Representation Learning over Multiple Knowledge Graphs for Knowledge
           Graphs Alignment
    • Abstract: Publication date: Available online 13 September 2018Source: NeurocomputingAuthor(s): Wenqiang Liu, Jun Liu, Mengmeng Wu, Samar Abbas, Wei Hu, Bifan Wei, Qinghua ZhengAbstractThe goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces. Mostly current works have demonstrated the benefits of knowledge graph embedding in single knowledge graph completion, such as relation extraction. The most significant distinction between multiple knowledge graphs embedding and single knowledge graph embedding is that the former must consider the alignments between multiple knowledge graphs which is very helpful to some applications built on multiple KGs, such as KB-QA and KG integration. In this paper, we proposed a new automatic representation learning model over Multiple Knowledge Graphs (MGTransE) by adopting a bootstrapping method. More specifically, MGTransE consists of three core components: Structure Model, Semantically Smooth Embedding Model and Iterative Smoothness Model. The experiment results on two real-world datasets show that our method achieves better performance on two new multiple KGs tasks compared with state-of-the-art KG embedding models and also preserves the key properties of knowledge graph embedding on traditional single KG tasks as compared to those methods learned from single KG.
       
  • A New Scalable Parallel Adder Based on Spiking Neural P Systems, Dendritic
           Behavior, Rules on the Synapses and Astrocyte-like Control to Compute
           Multiple Signed Numbers
    • Abstract: Publication date: Available online 12 September 2018Source: NeurocomputingAuthor(s): Thania Frias, Giovanny Sanchez, Luis Garcia, Marco Abarca, Carlos Diaz, Gabriel Sanchez, Hector PerezAbstractThis brief presents a scalable parallel neural adder circuit based on spiking neural P systems along with dendritic delays, dendritic feedback, rules on the synapses and astrocyte-like control to create a compact and highly scalable adder circuit. The proposed neural adder circuit adds multiple signed numbers either with few digits or with large number of digits in parallel employing a reduced number of neurons/synapses with simple and homogeneous spiking rules. The proposed neural adder was implemented in a DE0-Nano board (Altera Cyclone IV FPGA) to validate its performance. The results show that its implementation on a low-area low-cost FPGA requires small amount of circuitry. This potentially allows the development of highly parallel architectures that can be used in advanced applications, such as portable mobile robots, mobile devices, image and vision processing, among others.
       
  • Exploiting Cross-source Knowledge for Warming up Community Question
           Answering Services
    • Abstract: Publication date: Available online 11 September 2018Source: NeurocomputingAuthor(s): Yao Wan, Guandong Xu, Liang Chen, Zhou Zhao, Jian WuAbstractCommunity Question Answering (CQA) services such as Yahoo! Answers, Quora and StackOverflow are collaborative platforms where users can share and exchange their knowledge explicitly by asking and answering questions. One essential task in CQA is learning topical expertise of users, which may benefit many applications such as question routing and best answers identification. One limitation of existing related works is that they only consider the warm-start users who have posted many questions or answers, while ignoring cold-start users who have few posts. In this paper, we aim to exploit knowledge from cross sources such as GitHub and StackOverflow to build up the richer views of expertise for better CQA. Inspired by the idea of Bayesian co-training, we propose a topical expertise model from the perspective of multi-view learning. Specifically, we incorporate the consistency existing among multiple views into a unified probabilistic graphic model. Comprehensive experiments on two real-world datasets demonstrate the performance of our proposed model with the comparison of some state-of-the-art ones.
       
  • An event-triggered protocol for distributed optimal coordination of
           double-integrator multi-agent systems
    • Abstract: Publication date: Available online 4 September 2018Source: NeurocomputingAuthor(s): Dong Wang, Vijay Gupta, Wei WangAbstractThis paper designs two kinds of event-triggered control protocols for distributed optimal coordination of a group of agents with second order dynamics interacting according to given communication graph. First, a centralized event-triggered protocol is developed to accomplish the optimal coordination in the Pareto sense. The parameter is designed by constructing a new Lyapunov function. Second, it is shown that the event-triggered scheme is Zeno-free. Third, a distributed event-triggered protocol is devised by extending the proposed technique. Simulations are provided to demonstrate the effectiveness of the proposed design.
       
  • Learning Node and Edge Embeddings for Signed Networks
    • Abstract: Publication date: Available online 4 September 2018Source: NeurocomputingAuthor(s): Wenzhuo Song, Shengsheng Wang, Bo Yang, You Lu, Xuehua Zhao, Xueyan LiuAbstractMachine learning tasks for edges and nodes in networks heavily rely on feature engineering which requires expert knowledge and careful effort. Recent years, people become interested in the low dimensional vector representation of nodes and edges. However, existing methods on signed networks only aim to learn the node vectors, resulting in omitting edge information and extra effort to design edge vectors. In this work, we develop a framework for learning both nodes and edge vectors for signed networks. Thus, we can directly use edge vectors to represent the properties of the edges, and thereby improving the performance of link-oriented tasks. Our framework for learning network features is as below. We assume that there is a global mapping between the node and edge vector spaces. This assumption allows us to transform the problem into learning the mapping function and the node vectors. We propose node proximity for signed networks, a definition that is generalized from the second-order node proximity for unsigned networks. It provides a unified objective function that can preserve both the node and edge pattern of the network. Based on this definition, we propose two signed network representation methods. The first method is neural network signed network embedding (nSNE). It learns the node vectors and the mapping function via neural networks approach, which can uses the power of deep learning to fit with the data. The second method is light signed network embedding (lSNE). It specifies the mapping function as simply and linear function. It has fewer parameters to estimate and is equal to factorize both similarity and sign matrixes. We compare our methods with three state-of-the-art methods on four datasets. The results show that our methods are competitive.
       
  • Bayesian distance metric learning for discriminative fuzzy c-means
           clustering
    • Abstract: Publication date: Available online 4 September 2018Source: NeurocomputingAuthor(s): Negar Heidari, Zahra Moslehi, Abdolreza Mirzaei, Mehran SafayaniA great number of machine learning algorithms strongly depend on the underlying distance metric for representing the important correlations of input data. Distance metric learning is defined as learning an appropriate similarity or distance metric for all input data pairs. Metric learning algorithms are of supervised and unsupervised categories with different deterministic and probabilistic approaches. One of the objectives of unsupervised metric learning is to project data points into a new space in such a way that high clustering accuracy is provided. This is obtainable by maximizing between-clusters separation. There exist some deterministic metric learning methods to serve this purpose. In this article, a probabilistic method for unsupervised distance metric learning is proposed which aims to maximize the separability among different clusters in the projected space. In this proposed method, distance metric learning and fuzzy c-means clustering are jointly formulated in a sense that FCM provides clusters, and distance metric learning algorithm applies the obtained clusters to materialize the maximum separability among all; moreover, Markov Chain Monte Carlo (MCMC) algorithm is applied to infer the latent variables. This proposed method, not only can obtain a low dimensional projection with specified number of dimensions, but also it can learn the proper number of reduced dimensions for each dataset in an automated sense. The experimental results reveal the out-performance of this method on different real-world datasets against its counterparts.Graphical abstractGraphical abstract for this article
       
  • Epoch-incremental Dyna-learning and prioritized sweeping algorithms
    • Abstract: Publication date: Available online 4 September 2018Source: NeurocomputingAuthor(s): Roman ZajdelAbstractDyna-learning and prioritized sweeping (PS in short) are the most commonly used reinforcement learning algorithms which use the model of the environment. In this paper, the modified versions of these algorithms are presented. The modification exploits the breadth-first search (BFS) to conduct additional modifications of the policy in the epoch mode. The experiments, which are performed in the dynamic grid world and in the ball-beam system, showed that the proposed modifications improved the efficiency of the reinforcement learning algorithms.
       
  • Supervised low rank indefinite kernel approximation using minimum
           enclosing balls
    • Abstract: Publication date: Available online 3 September 2018Source: NeurocomputingAuthor(s): Frank-Michael Schleif, Andrej Gisbrecht, Peter TinoAbstractIndefinite similarity measures can be frequently found in bio-informatics by means of alignment scores, but are also common in other fields like shape measures in image retrieval. Lacking an underlying vector space, the data are given as pairwise similarities only. The few algorithms available for such data do not scale to larger datasets. Focusing on probabilistic batch classifiers, the Indefinite Kernel Fisher Discriminant (iKFD) and the Probabilistic Classification Vector Machine (PCVM) are both effective algorithms for this type of data but, with cubic complexity. Here we propose an extension of iKFD and PCVM such that linear runtime and memory complexity is achieved for low rank indefinite kernels. Employing the Nyström approximation for indefinite kernels, we also propose a new almost parameter free approach to identify the landmarks, restricted to a supervised learning problem. Evaluations at several larger similarity data from various domains show that the proposed methods provides similar generalization capabilities while being easier to parametrize and substantially faster for large scale data.
       
  • Unsupervised measure of Chinese lexical semantic similarity using
           correlated graph model for news story segmentation
    • Abstract: Publication date: Available online 3 September 2018Source: NeurocomputingAuthor(s): Wei Feng, Xuecheng Nie, Yujun Zhang, Lei Xie, Jianwu DangAbstractThis paper presents a simple yet effective approach to unsupervisedly measuring Chinese lexical semantic similarity, and shows its promising performance in automatic story segmentation of Mandarin broadcast news. Our approach centers on the unsupervised correlated affinity graph (UCAG) model, which is initialized as a hybrid sparse graph, encoding both explicit word-to-word contextual correlations and latent word-to-character correlations within the given corpus. The UCAG model further diffuses the initial sparse correlations throughout the graph by parallel affinity propagation. This provides us with a dense, reliable, and corpus-specific lexical semantic similarity measure, which comes from purely unlabeled data. We then generalize the classical cosine similarity metric to effectively take soft similarities into account for story segmentation. Extensive experiments on benchmark datasets validate the superiority of the proposed similarity measure over previous measures. We specifically show that our similarity measure averagely helps to achieve 7.7% relative F1-score improvement to the accuracy of state-of-art normalized cuts (NCuts) based story segmentation on two holistic benchmark Mandarin broadcast news corpora, TDT2 and CCTV, and achieves 10.8% relative F1-score improvement on the detailed broadcast news subsets.
       
  • Finite-time synchronization for delayed complex-valued neural networks via
           integrating inequality method
    • Abstract: Publication date: Available online 1 September 2018Source: NeurocomputingAuthor(s): Zhengqiu Zhang, Ailing Li, Shenghua YuAbstractIn this paper, we are concerned with the finite-time synchronization for a class of complex-valued neural networks with time delays. Instead of using some finite-time stability theorems which are recently widely applied to investigating the finite-time synchronization for neural networks, by means of using integral inequality method, two novel sufficient conditions on the finite-time synchronization for the above delayed complex-valued neural networks are established. Our results and method on finite-time synchronization for the above neural networks are new and complementary to the existing papers.
       
  • H +synchronization+of+coupled+reaction-diffusion+neural+networks&rft.title=Neurocomputing&rft.issn=0925-2312&rft.date=&rft.volume=">Analysis and adaptive control for lag H ∞ synchronization of coupled
           reaction-diffusion neural networks
    • Abstract: Publication date: Available online 1 September 2018Source: NeurocomputingAuthor(s): Qing Wang, Jin-Liang Wang, Shun-Yan Ren, Yan-Li HuangAbstractIn this paper, we respectively discuss the lag H∞ synchronization of coupled reaction-diffusion neural networks with state coupling and spatial diffusion coupling. Based on the inequality techniques and Lyapunov functional method, some lag H∞ synchronization criteria for these two coupled reaction-diffusion neural networks are obtained by exploiting the designed state feedback controllers. Furthermore, two adaptive strategies for ensuring the lag H∞ synchronization of coupled reaction-diffusion neural networks with state coupling and spatial diffusion coupling are also developed. Finally, the effectiveness of the proposed lag H∞ synchronization criteria is verified by two numerical examples.
       
  • Applying multi-label techniques in emotion identification of short texts
    • Abstract: Publication date: Available online 1 September 2018Source: NeurocomputingAuthor(s): Alex M.G. Almeida, Ricardo Cerri, Emerson Cabrera Paraiso, Rafael Gomes Mantovani, Sylvio Barbon JuniorAbstractSentiment Analysis is an emerging research field traditionally applied to classify opinions, sentiments and emotions towards polarity and subjectivity expressed in text. An important characteristic to automatic emotion analysis is the standpoint, in which we can look at an opinion from two perspectives, the opinion holder (author) who express an opinion, and the reader who reads and perceives the opinion. From the reader’s standpoint, the interpretations of the text can be multiple and depend on the personal background. The multiple standpoints cognition, in which readers can look at the same sentence, is an interesting scenario to use the multi-label classification paradigm in the Sentiment Analysis domain. This methodology is able to handle different target sentiments simultaneously in the same text, by also taking advantage of the relations between them. We applied different approaches such as algorithm adaptation, problem transformation and ensemble methods in order to explore the wide range of multi-label solutions. The experiments were conducted on 10,080 news sentences from two different real datasets. Experimental results showed that the Ensemble Classifier Chain overcame the other algorithms, average F-measure of 64.89% using emotion strength features, when considering six emotions and neutral sentiment.
       
  • Robust adaptive NN control of dynamically positioned vessels under input
           constraints
    • Abstract: Publication date: Available online 1 September 2018Source: NeurocomputingAuthor(s): Xin Hu, Jialu Du, Guibing Zhu, Yuqing SunAbstractA robust adaptive neural network (NN) control law is developed for dynamic positioning (DP) of vessels with unknown dynamics and unknown time-varying disturbances under input constraints through incorporating adaptive radial basis function (RBF) NNs, an auxiliary dynamic system and a robust control term into dynamic surface control method. The developed DP control law makes the DP closed-loop system be uniformly ultimately stable and the vessel's position and heading be maintained at the desired values with arbitrarily small errors. The advantages of the proposed control scheme are that: first, the developed DP control law does not require any priori knowledge of vessel dynamics and disturbances under input constraints, and prevents the presence of input constraints from degrading control performance and even destabilizing the DP control system; second, the developed DP control law compensates for not only unknown time-varying disturbances but also NN approximation errors for unknown vessel dynamics. Simulations on two supply vessels are conducted to exhibit the efficiency and control performance of the developed DP control law.
       
  • DMP-ELMs: Data and Model Parallel Extreme Learning Machines for
           Large-Scale Learning Tasks
    • Abstract: Publication date: Available online 1 September 2018Source: NeurocomputingAuthor(s): Yuewei Ming, En Zhu, Mao Wang, Yongkai Ye, Xinwang Liu, Jianping YinAbstractAs machine learning applications embrace larger data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands. Recently, several parallel variants of extreme learning machine (ELM) have been proposed, some of which are based on clusters. However, the limitation of computation and memory in these variants is still not well addressed when both the data and model are very large. Our goal is to build scalable ELMs with a large number of samples and hidden neurons, parallel running on clusters without computational and memory bottlenecks while having the same output results with the sequential ELM. In this paper, we propose two parallel variants of ELM, referred to as local data and model parallel ELM (LDMP-ELM) and global data and model parallel ELM (GDMP-ELM). Both variants are implemented on clusters with Message Passing Interface (MPI) environment. They both make a tradeoff between efficiency and scalability and have complementary advantages. Collectively, these two variants are called as data and model parallel ELMs (DMP-ELMs). The advantages of DMP-ELMs over existing variants are highlighted as follows: (1) They simultaneously utilize data and model parallel techniques to improve the parallelism of ELM. (2) They have the better scalability to support larger data and model due to that they have addressed the memory and computational bottlenecks appeared in existing variants. Extensive experiments conducted on four large-scale datasets show that our proposed algorithms have good scalability and achieve almost ideal speedup. To the best of our knowledge, it is the first time to successfully train a large ELM model with 50,000 hidden neurons on the mnist8m dataset with 8.1 million samples and 784 features.
       
  • TensorD: A tensor decomposition library in TensorFlow
    • Abstract: Publication date: Available online 1 September 2018Source: NeurocomputingAuthor(s): Liyang Hao, Siqi Liang, Jinmian Ye, Zenglin XuAbstractThe TensorD toolbox is a Python tensor library built on TensorFlow. It provides tensor decomposition methods as well as basic tensor operations. In addition, other features of TensorD include GPU compatibility, high modularity of structure, and open source. It facilitate the practice of tensor methods in computer vision, deep learning and other related research fields. The TensorD toolbox is available at https://github.com/Large-Scale-Tensor-Decomposition/tensorD.
       
  • Spectral clustering based on iterative optimization for large-scale and
           high-dimensional data
    • Abstract: Publication date: Available online 1 September 2018Source: NeurocomputingAuthor(s): Yang Zhao, Yuan Yuan, Feiping Nie, Qi WangAbstractSpectral graph theoretic methods have been a fundamental and important topic in the field of manifold learning and it has become a vital tool in data clustering. However, spectral clustering approaches are limited by their computational demands. It would be too expensive to provide an optimal approximation for spectral decomposition in dealing with large-scale and high-dimensional data sets. On the other hand, the rapid development of data on the Web has posed many rising challenges to the traditional single-task clustering, while the multi-task clustering provides many new thoughts for real-world applications such as video segmentation. In this paper, we will study a Spectral Clustering based on Iterative Optimization (SCIO), which solves the spectral decomposition problem of large-scale and high-dimensional data sets and it well performs on multi-task clustering. Extensive experiments on various synthetic data sets and real-world data sets demonstrate that the proposed method provides an efficient solution for spectral clustering.
       
  • A Hybrid Feature Model and Deep Learning Based Fault Diagnosis for
           Unmanned Aerial Vehicel Sensors
    • Abstract: Publication date: Available online 1 September 2018Source: NeurocomputingAuthor(s): Dingfei Guo, Maiying Zhong, Hongquan Ji, Yang Liu, Rui YangAbstractFault diagnosis plays an important role in guaranteeing system safety and reliability for unmanned aerial vehicles (UAVs). In this study, a hybrid feature model and deep learning based fault diagnosis for UAV sensors is proposed. The residual signals of different sensor faults, including global positioning system (GPS), inertial measurement unit (IMU), air data system (ADS), were collected. This paper used short time fourier transform (STFT) to transform the residual signal to the corresponding time-frequency map. Then, a convolutional neural network (CNN) was used to extract the feature of the map and the fault diagnosis of the UAV sensors was implemented. Finally, the performance of the proposed methodology is evaluated through flight experiments of the UAV. From the visualization, the sensor faults information can be extracted by CNN and the fault diagnosis logic between the residuals and the health status can be constructed successfully.
       
  • Fast Subspace Segmentation via Random Sample Probing
    • Abstract: Publication date: Available online 31 August 2018Source: NeurocomputingAuthor(s): Yang Li, Yubao Sun, Qingshan Liu, Shengyong ChenAbstractSubspace segmentation is to group a given set of n data points into multiple clusters, with each cluster corresponding to a subspace. Prevalent methods such as Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR) are effective in terms of segmentation accuracy, but computationally inefficient while applying to gigantic datasets where n is very large as they possess a complexity of O(n3). In this paper, we propose an iterative method called Random Sample Probing (RANSP). In each iteration, RANSP finds the members of one subspace by randomly choosing a data point (called “seed”) at first, and then using Ridge Regression (RR) to retrieve the other points that belong to the same subspace as the seed. Such a procedure is repeated until all points have been classified. RANSP has a computational complexity of O(n) and can therefore handle large-scale datasets. Experiments on synthetic and real datasets confirm the effectiveness and efficiency of RANSP.
       
  • Consensus seeking in heterogeneous second-order multi-agent systems with
           switching topologies and random link failures
    • Abstract: Publication date: Available online 31 August 2018Source: NeurocomputingAuthor(s): Yuhua Cheng, Yangzhen Zhang, Lei Shi, Jinliang Shao, Yue XiaoAbstractIn this paper, we consider the issue of consensus among the agents with heterogeneous dynamics under random link failures that obey Bernoulli distribution. The heterogeneous multi-agent systems (MASs) are composed of hybrid dynamic agents including first-order dynamic agents and second-order dynamic agents, and the communication topologies are assumed to be directed and time-varying. The failure control protocol is designed by randomly utilizing the previous one- or two-step states information sent by the neighbors. A criterion for the heterogeneous consensus is established by constructing augmented systems with nonnegative random coefficient matrices for the switching topologies. With the help of graph theory and stochastic matrix theory, a sufficient condition related to switching topologies is established to ensure the realization of heterogeneous consensus. Numerical examples are finally provided to validate the theoretical result.
       
  • Bayesian tensor factorization for multi-way analysis of multi-dimensional
           EEG
    • Abstract: Publication date: Available online 31 August 2018Source: NeurocomputingAuthor(s): Yunbo Tang, Dan Chen, Lizhe Wang, Albert Y. Zomaya, Jingying Chen, Honghai LiuAbstractFactorization-based analysis of multi-dimensional EEG (Electroencephalography) has become increasingly important in neuroscience research and practices with the capability of extracting latent multi-way features. However, how to sift the most informative factors of routinely noisy EEG remains unclear especially under the circumstance of no a priori knowledge. This study proposes a Bayesian tensor factorization (BTF) model as a “one-stop” solution to the challenges. BTF assumes non-informative priori on potential distribution of factors and noise derived from exponential family distribution. A high-dimensional variational Bayesian inference method is designed to iteratively estimate the posterior distribution of potential factors. The factor vectors whose elements are “small” values can then be identified as redundancy and filtered out afterwards. Finally, the study enables a generic factorization-based method for multi-way analysis of brain states. Results from experiments on synthesized tensors indicate that (1) BTF excels in processing EEG tensor mixed with intensive white noises in comparison with the traditional counterparts; and (2) the non-informative components in factors can be filtered out effectively (rank reduction). Two case studies of factorization-based multi-way analysis assisted by a Multi-Layer Perception (MLP) network have been performed on two real EEG datasets, and (1) seizure detection and (2) sleep stage classification can be achieved with averaged accuracy up to 99.52% and 90.81% respectively.
       
  • DCT based weighted adaptive multi-linear data completion and denoising
    • Abstract: Publication date: Available online 31 August 2018Source: NeurocomputingAuthor(s): Baburaj Madathil, Sudhish N. GeorgeAbstractThis paper emphasises on formulating a weighted adaptive transform based solution for multi-linear signal completion and denoising problems based on the fact that the real-valued DCT based tensor algebra provides better low-rank representation compared with the existing Fourier transform based framework. Using an m-mode DCT based tensor SVD, complementary information existing in all modes of the tensor is effectively employed to achieve better performance. Further improvement in the tensor recovery is accomplished by adaptive low-rank regularization via measuring the degree of the low-rank structure existing in each mode. The proposed method follows adaptive low rank regularization strategy which provides more gravitas to the better low-rank representation. The proposed algorithm built by combining the three aspects of tensor processing such as, DCT based tensor SVD, utilization of complementary information from all the modes of the tensor and adaptive low-rank regularization to attain greater signal recovery. The performance of the proposed method is evaluated by applying to video completion and denoising problems.
       
  • Semantic scene completion with dense CRF from a single depth image
    • Abstract: Publication date: Available online 31 August 2018Source: NeurocomputingAuthor(s): Liang Zhang, Le Wang, Xiangdong Zhang, Peiyi Shen, Mohammed Bennamoun, Guangming Zhu, Syed Afaq Ali Shah, Juan SongAbstractScene understanding is a significant research topic in computer vision, especially for robots to understand their environment intelligently. Semantic scene segmentation can help robots to identify the objects that are present in their surroundings, while semantic scene completion can enhance the ability of the robot to infer the object shape, which is pivotal for several high-level tasks. With dense Conditional Random Field (CRF), one key issue is how to construct the long-range interactions between nodes with Gaussian pairwise potentials. Another issue is what effective and efficient inference algorithms can be adapted to resolve the optimization. In this paper, we focus on semantic scene segmentation and completion optimization technology simultaneously using dense CRF based on a single depth image only. Firstly, we convert the single depth image into different down-sampled Truncated Signed Distance Function (TSDF) or flipped TSDF voxel formats, and formulate the pairwise potentials terms with such a representation. Secondly, we use the output results of an end-to-end 3D convolutional neural network named SSCNet to obtain the unary potentials. Finally, we pursue the efficiency of different CRF inference algorithms (the mean-field inference, the negative semi-definite specific difference of convex relaxation, the proximal minimization of linear programming and its variants, etc.). The proposed dense CRF and inference algorithms are evaluated on three different datasets (SUNCG, NYU, and NYUCAD). Experimental results demonstrate that the voxel-level intersection over union (IoU) of predicted voxel’s semantic and completion can reach to state-of-the-art. Specifically, for voxel semantic segmentation, the highest IoU improvements are 2.6%, 1.3%, 3.1%, and for scene completion, the highest IoU improvements are 2.5%, 3.7%, 5.4%, respectively for SUNCG, NYU, and NYUCAD datasets.
       
  • Feature selection for multi-label learning based on kernelized fuzzy rough
           sets
    • Abstract: Publication date: Available online 31 August 2018Source: NeurocomputingAuthor(s): Yuwen Li, Yaojin Lin, Jinghua Liu, Wei Weng, Zhenkun Shi, Shunxiang WuAbstractFeature selection is an essential pre-processing part in multi-label learning. Multi-label learning is usually used to deal with many complicated tasks, in which each sample is associated with multiple labels simultaneously. Fuzzy rough set model is one of the most effective ways for multi-label learning. However, it treats feature space and label space separately, and only uses features to describe sample structure information. In this paper, we fully consider the internal correlation between feature space and label space while fusing kernelized information from respective spaces. Moreover, we integrate fuzzy rough set with multiple kernel learning to finally realize feature selection. To be specific, firstly, we leverage one kind of kernel function to reveal the similarity between samples in feature space, and another one to assess the degree of label overlap between samples in label space. Secondly, we combine the kernelized information from the two spaces through linear combination to achieve precisely the lower approximation and construct a robust multi-label kernelized fuzzy rough set model, called RMFRS in this paper. Meanwhile, we discuss its properties and give theoretical analysis. Finally, we define a measurement criterion for selecting optimal features to evaluate the performance of the proposed algorithm. As many as 10 publicly available data sets are used to validate the effectiveness of our methods, and the result shows a distinct advantage over the state-of-the-art.
       
  • Multiple kernel locality-constrained collaborative representation-based
           discriminant projection for face recognition
    • Abstract: Publication date: Available online 31 August 2018Source: NeurocomputingAuthor(s): Zhichao Zheng, Huaijiang Sun, Guoqing ZhangAbstractCollaborative representation-based classifier (CRC) has achieved superior classification performance in the field of face recognition. However, the performance of CRC will degrade significantly when facing non-linear structural data. To address this problem, many kernel CRC (KCRC) methods have been proposed. These methods usually use a predetermined kernel function which is difficult to be selected. In addition, how to select appropriate parameters remains a challenging problem. Hence, multiple kernel technology (MKL) is applied on CRC, which called MK-CRC. However, it only considers the representation errors while ignoring the class label information in the training process. In this paper, we propose a multiple kernel locality-constrained collaborative representation-based classifier (MKLCRC) which is the multiple kernel extension of CRC and considers the local structures of data. Based on the classification rule of MKLCRC, we propose a dimensionality reduction (DR) method called multiple kernel locality-constrained collaborative representation-based discriminant projection (MKLCR-DP). The goal of MKLCR-DP is to learn a projection matrix and a set of kernel weights to generate a low-dimensional subspace where the between-class reconstruction errors are maximized and the within-class reconstruction errors are minimized. Thus MKLCRC can achieve better performance in this low-dimensional subspace. The proposed method can be efficiently optimized with the trace ratio optimization. Experiments on AR, extended Yale B, FERET, CMU PIE and LFW face databases demonstrate that our method outperforms related state-of-the-art algorithms.
       
  • Unsupervised pixel-wise classification for Chaetoceros image
           segmentation
    • Abstract: Publication date: Available online 31 August 2018Source: NeurocomputingAuthor(s): Ning Tang, Fei Zhou, Zhaorui Gu, Haiyong Zheng, Zhibin Yu, Bing ZhengAbstractChaetoceros is a dominant genus of marine planktonic diatoms with worldwide distribution. Due to the difficulty of extracting setae from Chaetoceros images, automatic segmentation of Chaetoceros is still a challenging task. In this paper, we address this difficult task by regarding the whole segmentation process as unsupervised pixel-wise classification without human participation. First, we automatically produce positive (object) and negative (background) samples for follow-up training, by combining the advantages of two image processing algorithms: Grayscale Surface Direction Angle Model (GSDAM) for extracting setae information and Canny for detecting cell edges from low-contrast and strong-noisy microscopic images. Second, we develop pixel-wise training by using the produced samples in the training process of Deep Convolutional Neural Network (DCNN). At last, the trained DCNN is used to label other pixels into object and background for final segmentation. We compare our method with eight mainstream segmentation approaches: Otsu’s thresholding, Canny, Watershed, Mean Shift, gPb-owt-ucm, Normalized Cut, Efficient Graph-based method and GSDAM. To objectively evaluate segmentation results, we apply six well-known evaluation indexes. Experimental results on a new Chaetoceros image dataset with human labelled ground truth show that our method outperforms the eight mainstream segmentation methods in terms of both quantitative and qualitative evaluation.
       
  • LSTM-based traffic flow prediction with missing data
    • Abstract: Publication date: Available online 31 August 2018Source: NeurocomputingAuthor(s): Yan Tian, Kaili Zhang, Jianyuan Li, Xianxuan Lin, Bailin YangAbstractTraffic flow prediction plays a key role in intelligent transportation systems. However, since traffic sensors are typically manually controlled, traffic flow data with varying length, irregular sampling and missing data are difficult to exploit effectively. To overcome this problem, we propose a novel approach that is based on Long Short-Term Memory (LSTM) in this paper. In addition, the multiscale temporal smoothing is employed to infer lost data and the prediction residual is learned by our approach. We demonstrate the performance of our approach on both the Caltrans Performance Measurement System (PeMS) data set and our own traffic flow data set. According to the experimental results, our approach obtains higher accuracy in traffic flow prediction compared with other approaches.
       
  • Modified bidirectional extreme learning machine with Gram–Schmidt
           orthogonalization method
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Guoqiang Zeng, Baihai Zhang, Fenxi Yao, Senchun ChaiAbstractIncremental extreme learning machine has been proved to be an efficient and simple universal approximator. However, the network architecture may be very large due to the inefficient nodes which have a tiny effect on reducing the residual error. More to the point, the output weights are not the least square solution. To reduce such inefficient nodes, a method called bidirectional ELM (B-ELM), which analytically calculates the input weights of even nodes, was proposed. By analyzing, B-ELM can be further improved to achieve better performance on compacting structure. This paper proposes the modified B-ELM (MB-ELM), in which the orthogonalization method is involved in B-ELM to orthogonalize the output vectors of hidden nodes and the resulting vectors are taken as the output vectors. MB-ELM can greatly diminish inefficient nodes and obtain a preferable output weight vector which is the least square solution, so that it has better convergence rate and a more compact network architecture. Specifically, it has been proved that in theory, MB-ELM can reduce residual error to zero by adding only two nodes into network. Simulation results verify these conclusions and show that MB-ELM can reach smaller low limit of residual error than other I-ELM methods.
       
  • Robust 2DLDA based on correntropy
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Fujin Zhong, Li Liu, Jun HuAbstractTo further improve the robustness of two-dimensional LDA (2DLDA) methods against outliers, this paper proposes a new robust 2DLDA version which obtains the optimal projection transformation by maximizing the correntropy-based within-class similarity and maintaining the global dispersity simultaneously. The objective problem of the proposed method can be solved by an iterative optimization algorithm which is proved to converge at a local maximum point. The experimental results on FERET face database, PolyU palmprint database and Binary Alphadigits database illustrate that the proposed method outperforms three conventional 2DLDA methods when there are outliers.
       
  • Symmetric low-rank representation with adaptive distance penalty for
           semi-supervised learning
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Chang-Peng Wang, Jiang-She Zhang, Fang Du, Guang ShiAbstractGraph-based approaches have been successfully used in semi-supervised learning (SSL) by weighting the affinity between the corresponding pairs of samples. Low-rank representation (LRR) is one of the state-of-the-art methods, has been extensively employed in the existing graph-based learning models. In graph-based SSL, a weighted affinity graph induced by LRR coefficients is used to reflect the relationships among data samples. Constructing a robust and discriminative graph to discover the intrinsic structures of the data is critical for SSL, because the central idea behind the graph-based SSL approaches is to explore the pairwise affinity between data samples to infer those unknown labels. However, most of existing LRR-based approaches fail to guarantee weight consistency for each pair of data samples, which is beneficial to preserve the subspace structures of high dimensional data. In this paper, we propose a symmetric LRR with adaptive distance penalty (SLRRADP) method for the small sample size (SSS) recognition problem. The graph identified by SLRRADP can not only preserve global and local structures of data, but also exploit intrinsic correlation information of data samples. Extensive experimental results on semi-supervised classification tasks demonstrate the effectiveness of the proposed method and its superiority in performance over the state-of-the-art graph construction approaches.
       
  • Semantic softmax loss for zero-shot learning
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Zhong Ji, Yuxin Sun, Yunlong Yu, Jichang Guo, Yanwei PangAbstractA typical pipeline for Zero-Shot Learning (ZSL) is to integrate the visual features and the class semantic descriptors into a multimodal framework with a linear or bilinear model. However, the visual features and the class semantic descriptors locate in different structural spaces, a linear or bilinear model can not capture the semantic interactions between different modalities well. In this letter, we propose a nonlinear approach to impose ZSL as a multi-class classification problem via a Semantic Softmax Loss by embedding the class semantic descriptors into the softmax layer of multi-class classification network. To narrow the structural differences between the visual features and semantic descriptors, we further use an L2 normalization constraint to the differences between the visual features and visual prototypes reconstructed with the semantic descriptors. The results on four benchmark datasets, i.e., AwA, CUB, SUN and ImageNet demonstrate the proposed approach can boost the performances steadily and achieve the state-of-the-art performance for both zero-shot classification and zero-shot retrieval.
       
  • Saliency detection via conditional adversarial image-to-image network
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Yuzhu Ji, Haijun Zhang, Q.M. Jonathan WuAbstractRecent research has shown that conditional adversarial network (cGAN) can be adopted to perform image-to-image translation task effectively. Saliency detection is another challenging computer vision task to model human vision attention mechanism. By reformulating the saliency detection task, in this work, we propose to conduct saliency detection by exploiting conditional adversarial network under the cGAN framework, in which saliency map prediction is transformed as a saliency segmentation task by using pair-wised image-to-ground-truth saliency. To further investigate the potential of cGAN for saliency detection, we train the cGAN model to capture saliency-to-context information by translating saliency mask to real image. Experimental results confirm that the trained generator can achieve comparable state-of-the-art performance on saliency segmentation, and can generate reasonable results for saliency-to-image translation.
       
  • Linear discriminants described by disjoint tangent configurations
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): José-Luis Sancho-Gómez, Juan-Antonio Martínez-García, Stanley C. Ahalt, Aníbal R. Figueiras-VidalAbstractIn this paper, a new interpretation of parametric linear discriminants for binary classification problems is presented. Linear discriminants are described in terms of Disjoint Tangent Configurations (DTC) established between the ellipsoidal level surfaces resulting from the means and covariance matrices of the distributions. This is a new framework that allows, first, a new interpretation and analysis of several well-known linear discriminants and, second, the design of new discriminants with very interesting properties. In particular, it is shown that the analytical expression of the Bayes Linear Discriminant —whose explicit expression is still unknown— can be derived from a particular DTC. Besides the Bayes discriminant, other classical linear discriminants are also described according to the DTC analysis, in particular, the Fisher and the Scatter-based Linear Discriminants. On the other hand, two new linear discriminants for the minimax and the Bayesian solutions are obtained from the DTC analysis. Both have a direct analytical expression in contrast to the existing iterative solutions, with which they are compared. The first DTC discriminant, which is called MPDH-DTC, is the solution of the Minimax Probabilistic Decision Hyperplane (MPDH) problem, the same solution that the Minimax Probability Machine (MPM) method approximates by an iterative convex optimization. The second discriminant, called Quasi-Bayes-DTC Linear Discriminant, is designed to be an approximation to the Bayes Linear Discriminant, which requires a search procedure to find the solution.Considering both the accuracy over several synthetic and real problems and the computational cost, the Quasi-Bayes-DTC is the preferred discriminant due to its high performance and low computational cost, unless a minimax solution is required, in that case the MPDH-DTC is preferred.
       
  • Evaluation of deep neural networks for traffic sign detection systems
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Álvaro Arcos-García, Juan A. Álvarez-García, Luis M. Soria-MorilloAbstractTraffic sign detection systems constitute a key component in trending real-world applications, such as autonomous driving, and driver safety and assistance. This paper analyses the state-of-the-art of several object-detection systems (Faster R-CNN, R-FCN, SSD, and YOLO V2) combined with various feature extractors (Resnet V1 50, Resnet V1 101, Inception V2, Inception Resnet V2, Mobilenet V1, and Darknet-19) previously developed by their corresponding authors. We aim to explore the properties of these object-detection models which are modified and specifically adapted to the traffic sign detection problem domain by means of transfer learning. In particular, various publicly available object-detection models that were pre-trained on the Microsoft COCO dataset are fine-tuned on the German Traffic Sign Detection Benchmark dataset. The evaluation and comparison of these models include key metrics, such as the mean average precision (mAP), memory allocation, running time, number of floating point operations, number of parameters of the model, and the effect of traffic sign image sizes. Our findings show that Faster R-CNN Inception Resnet V2 obtains the best mAP, while R-FCN Resnet 101 strikes the best trade-off between accuracy and execution time. YOLO V2 and SSD Mobilenet merit a special mention, in that the former achieves competitive accuracy results and is the second fastest detector, while the latter, is the fastest and the lightest model in terms of memory consumption, making it an optimal choice for deployment in mobile and embedded devices.
       
  • An improved locality preserving projection with ℓ1-norm minimization for
           dimensionality reduction
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Weizhong Yu, Rong Wang, Feiping Nie, Fei Wang, Qiang Yu, Xiaojun YangAbstractLocality preserving projection (LPP) is a classical tool for dimensionality reduction and feature extraction. It usually makes use of the ℓ2-norm criterion for optimization, and is thus sensitive to outliers. In order to achieve robustness, LPP-L1 is proposed by employing the ℓ1-norm as distance criterion. However, the edge weights of LPP-L1 measure only the dissimilarity of pairs of vertices and ignore the preservation of the similarity. In this paper, we develop a novel algorithm, termed as ILPP-L1, in which the ℓ1-norm is utilized to obtain robustness and the similarities of pairs of vertices are effectively preserved, simultaneously. ILPP-L1 is robust to outliers because of the use of the ℓ1-norm. The ℓ1-norm minimization problem is directly solved, which ensures the preservation of the similarity of pairs of vertices. The solution is justified to converge to local minimum. In addition, ILPP-L1 avoids small sample size problem. Experiment results on benchmark databases confirm the effectiveness of the proposed method.
       
  • Training a robust reinforcement learning controller for the uncertain
           system based on policy gradient method
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Zhan Li, Shengri Xue, Weiyang Lin, Mingsi TongAbstractThe target of this paper is to design a model-free robust controller for uncertain systems. The uncertainties of the control system mainly consists of model uncertainty and external disturbance, which widely exist in the practical utilization. These uncertainties will negatively influence the system performance and this motivates us to train a model-free controller to solve this problem. Reinforcement learning is an important branch of machine learning and is able to achieve well performed control results by optimizing a policy without the knowledge of mathematical plant model. In this paper, we construct a reward function module to describe the specific environment of the concerned system, taking uncertainties into account. Then we utilize a new policy gradient method to optimize the policy and implement this algorithm with the actor-critic structure neuro networks. These two networks are our reinforcement learning controllers. Finally, we illustrate the applicability and efficiency of the proposed method by applying it on an experimental helicopter platform model, which includes model uncertainties and external disturbances.
       
  • Fast, robust and accurate posture detection algorithm based on Kalman
           filter and SSD for AGV
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Weiyang Lin, Xinyang Ren, Jianjun Hu, Yuzhe He, Zhan Li, Mingsi TongAbstractThe autonomous navigation technology of mobile robot based on visual sensor has been widely studied by researchers in recent years. Visual sensors, such as Charge-coupled Device (CCD), usually bring severe noise and unpredictable disturbances (including light differences, scene changes, etc.), thus it is necessary to find an adapted detection method to accommodate to the complex missions. Traditional detection model obtains feature characterization manually, which is laborious, time-consuming, mostly depending on researchers experience, and greatly increases the complexity of the recognition procedures. In this paper, we propose a target location strategy Kalman based SSD (K-SSD) utilizing convolution neural network (CNN) to improve the location accuracy and the speed of mobile robot during the automatic navigation. First, one frame of the entire scene is captured by a camera to construct an environment model. Then, a Single Shot MultiBox Detector (SSD) model is trained offline using original images as model input, which can output classes corresponding with their own positions. Finally, we use the Kalman Filter to filter the Gaussian noise to improve the accuracy of location. In the experiments, we use the HUSKY UGV platform to verify the proposed strategy. The results indicate that this algorithm is capable of realizing the fast, robust and accurate posture detection for Gaussian noise and abnormal noise.
       
  • Stability analysis of time varying delayed stochastic Hopfield neural
           networks in numerical simulation
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Linna Liu, Feiqi DengAbstractThis paper is concerned with the stability analysis of time varying delayed stochastic Hopfield neural networks in numerical simulation . To achieve our expected conclusions, we will reform the classical contractive mapping principle in functional analysis, with some modifications, to adapt to our conditions and both the continuous and the discrete delayed models. Under the reasonable conditions, it is shown that, the Euler–Maruyama numerical scheme is mean square exponentially stable of exact solution dependent of step size. Further more, it is also shown that the backward Euler–Maruyama numerical scheme can share the mean square exponential stability of the exact solution independent of step size under the same conditions.
       
  • Finite-time synchronization of fractional-order memristive recurrent
           neural networks with discontinuous activation functions
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Xiaofan Li, Jian-an Fang, Wenbing Zhang, Huiyuan LiAbstractThis paper is concerned with the finite-time synchronization for a class of drive-response fractional-order memristive recurrent neural networks with discontinuous activation functions. By using the theories of fractional-order differential inclusions and set-valued map, the finite-time synchronization problem for a class of drive-response fractional-order memristive recurrent neural networks with discontinuous activation functions is formulated under the framework of Filippov solution. Then, two novel state feedback controllers are designed according to state feedback control technique. In particular, based on the fractional Lyapunov stability theory, the finite-time stability theory and Young inequality, some novel algebraic synchronization criteria are obtained to ensure the finite-time synchronization of a class of drive-response fractional-order memristive recurrent neural networks with discontinuous activation functions. Moreover, we give the estimation of the upper bound of the settling time for synchronization. Finally, a simulation example is given to show the effectiveness of our theoretical results.
       
  • Consensus problem in multi-agent systems under delayed information
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Zhenhua Wang, Juanjuan Xu, Xinmin Song, Huaxiang ZhangAbstractThis paper studies the consensus problem of first-order multi-agent systems with unknown communication delay. Given unstable agent dynamics and directed network topology, consensus conditions upon two kinds of communication delays are provided. When the relative information is affected by delay, an allowable delay bound for consensus is obtained; if delay influences neighbors’ transmitted information, sufficient consensus condition admitting any large yet bounded delay is acquired. It is observed in particular that when the network topology is undirected, the delay is allowed to be time-varying. Finally, two numerical examples are carried out to demonstrate the effectiveness of the theoretical results.
       
  • Image super-resolution via a densely connected recursive network
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Zhanxiang Feng, Jianhuang Lai, Xiaohua Xie, Junyong ZhuAbstractThe single-image super-resolution techniques (SISR) have been significantly promoted by deep networks. However, the storage and computation complexities of deep models increase dramatically alongside with the reconstruction performance. This paper proposes a densely connected recursive network (DCRN) to trade off the performance and complexity. We introduce an enhanced dense unit by removing the batch normalization (BN) layers and employing the squeeze-and-excitation (SE) structure. A recursive architecture is also adopted to control the parameters of deep networks. Moreover, a de-convolution based residual learning method is proposed to accelerate the residual feature extraction process. The experimental results validate the efficiency of the proposed approach.
       
  • Approximation capability of two hidden layer feedforward neural networks
           with fixed weights
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Namig J. Guliyev, Vugar E. IsmailovAbstractWe algorithmically construct a two hidden layer feedforward neural network (TLFN) model with the weights fixed as the unit coordinate vectors of the d-dimensional Euclidean space and having 3d+2 number of hidden neurons in total, which can approximate any continuous d-variable function with an arbitrary precision. This result, in particular, shows an advantage of the TLFN model over the single hidden layer feedforward neural network (SLFN) model, since SLFNs with fixed weights do not have the capability of approximating multivariate functions.
       
  • Response selection with topic clues for retrieval-based chatbots
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Yu Wu, Zhoujun Li, Wei Wu, Ming ZhouAbstractWe consider incorporating topic information into message-response matching to boost responses with rich content in retrieval-based chatbots. To this end, we propose a topic aware attentive recurrent neural network in which representations of the message and the response are enhanced by the topic information. The model first leverages the message and the response represented by recurrent neural networks (RNNs) to weight topic words given by a pre-trained LDA model and forms topic vectors as linear combinations of the topic words. It then refines the representations of the message and the response with the topic vectors through an attention mechanism. The attention mechanism weights the hidden sequences of the message and the response not only by themselves but also by their topic vectors. Thus both the parts that are important to matching and the parts that are semantically related to the topics are highlighted in the representations.Empirical studies on public data and human annotated data show that our model can significantly outperform state-of-the-art methods and rank more responses with rich content in high positions.
       
  • Deep vanishing component analysis network for pattern classification
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Hongliang Yan, Zifei Yan, Gang Xiao, Weizhi Wang, Wangmeng ZuoAbstractConvolutional neural networks (CNN) have achieved great success in image classification, object detection and semantic segmentation. Generally, CNN is stacked by layers of convolutional filtering, entry-wise nonlinearities, and pooling operators, where convolutional filtering and pooling can be regarded as the operators for feature transformation and dimensionality reduction, respectively. However, CNN was suggested for images and signals, and cannot be used to samples in general vector form. Motivated by the CNN structure, in this paper we propose a deep vanishing component analysis network (DVN) for pattern classification of samples in general vector form. To be specific, vanishing component analysis is utilized for non-linear feature transformation and principal component analysis for dimensionality reduction, while Gentle Adaboost is employed for entry-wise nonlinearities and feature selection. DVN can thus utilize the multi-layer network architecture stacked by VCA, PCA and Gentle Adaboost for pattern classification. Experimental results show that our DVN significantly outperforms the existing surficial learning methods, e.g., SVM, and is comparable or better than several deep learning approaches, e.g., DBN and DBM.
       
  • Gaussian process regression method for forecasting of mortality rates
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Ruhao Wu, Bo WangAbstractGaussian process regression (GPR) has long been shown to be a powerful and effective Bayesian nonparametric approach, and has been applied to a wide range of fields. In this paper we present a new application of Gaussian process regression methods for the modelling and forecasting of human mortality rates. The age-specific mortality rates are treated as time series and are modelled by four conventional Gaussian process regression models. Furthermore, to improve the forecasting accuracy we propose to use a weighted mean function and the spectral mixture covariance function in the GPR model. The numerical experiments show that the combination of the weighted mean function and the spectral mixture covariance function provides the best performance in forecasting long term mortality rates. The performance of the proposed method is also compared with three existing models in the mortality modelling literature, and the results demonstrate that the GPR model with the weighted mean function and the spectral mixture covariance function provides a more robust forecast performance.
       
  • “Parallel Training Considered Harmful'”: Comparing series-parallel
           and parallel feedforward network training
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Antônio H. Ribeiro, Luis A. AguirreAbstractNeural network models for dynamic systems can be trained either in parallel or in series-parallel configurations. Influenced by early arguments, several papers justify the choice of series-parallel rather than parallel configuration claiming it has a lower computational cost, better stability properties during training and provides more accurate results. Other published results, on the other hand, defend parallel training as being more robust and capable of yielding more accurate long-term predictions. The main contribution of this paper is to present a study comparing both methods under the same unified framework with special attention to three aspects: (i) robustness of the estimation in the presence of noise; (ii) computational cost; and, (iii) convergence. A unifying mathematical framework and simulation studies show situations where each training method provides superior validation results and suggest that parallel training is generally better in more realistic scenarios. An example using measured data seems to reinforce such a claim. Complexity analysis and numerical examples show that both methods have similar computational cost although series-parallel training is more amenable to parallelization. Some informal discussion about stability and convergence properties is presented and explored in the examples.
       
  • Classifier selection and clustering with fuzzy assignment in ensemble
           model for credit scoring
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Haoting Zhang, Hongliang He, Wenyu ZhangAbstractWith the development of statistical methods and machine learning algorithms, credit scoring is no longer a task merely based on experience. From single base classifiers to ensemble classifiers and hybrid models, researches have been focusing on combining classifiers and hybridizing with artificial intelligence algorithms to improve performance of the models. Ensemble classifiers have been proven to have a better predictive accuracy than single classifiers, but the method of ensemble affects performance and is worth studying. This study is based on the ensemble of five of the most widely recognized base classifiers in credit scoring, i.e. logistic regression, support vector machine, neural network, gradient boosting decision tree and random forest. It proposes a new method of selecting classifiers using Genetic Algorithm after they are trained, considering both the accuracy and diversity of the ensemble. Besides, unsupervised clustering is integrated with a fuzzy assignment procedure in the model, to make more use of the data pattern and improve performance. The proposed CF-GA-Ens model is tested on three credit scoring datasets (Australian, German, Japanese) and three performance measures (accuracy, AUC, F-score), and the results show that our classifier selection and clustering procedures have a positive impact on all performance measures.
       
  • F-score feature selection based Bayesian reconstruction of visual image
           from human brain activity
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Wei Huang, Hongmei Yan, Ran Liu, Lixia Zhu, Huangbin Zhang, Huafu ChenAbstractDecoding perceptual experience from human brain activity is a big challenge in neuroscience. Recent advances in human neuroimaging have shown that it is possible to reconstruct a person’s visual experience based on the retinotopy in the early visual cortex and the multivariate pattern analysis (MVPA) method using functional magnetic resonance imaging (fMRI). Previous researches reconstructed binary contrast-defined images using combination of multi-scale local image decoders in V1, V2 and V3, where contrast for local image bases was predicted from fMRI activity by sparse multinomial logistic regression (SMLR) and other models. However, the precision and efficiency of the visual image reconstruction remain insufficient. Proper feature selection is widely known to be as critical for prediction and reconstruction. Aiming at the shortcomings of existing reconstruction models, we proposed a new model of Bayesian reconstruction based on F-score feature selection (Bayes+F). The results indicate that the proposed Bayes+F model has better reconstruction accuracy and higher efficiency than the SMLR and other models, showing better robustness and noise resistant ability. It can improve the spatial correlation coefficient (Mean  ±  variance: 0.7078  ±  0.2104) and decrease the standard error (Mean  ±  variance: 0.2693  ±  0.0871) between the stimulus and the reconstructed image. Furthermore, the proposed model can reconstruct the images extremely rapid, 100 times faster than SMLR does.
       
  • Matrix completion with capped nuclear norm via majorized proximal
           minimization
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Shenfen Kuang, Hongyang Chao, Qia LiAbstractWe investigate the problem of matrix completion with capped nuclear norm regularization. Different from most existing regularizations that minimize all the singular values simultaneously, capped nuclear norm only penalties the singular values smaller than certain threshold. Due to its non-smoothness and non-convexity, by formulating with Majorization Minimization (MM) approach, we develop a fast Majorized Proximal Minimization Impute (MPM-Impute) algorithm. At each iteration, the sub-problem is relaxed to a surrogate (upper bound) function and solved via proximal minimization with closed form solution. Though it requires singular value decompositions (SVD) at each iteration, by incorporating with the randomized algorithm, we propose the Randomized Truncated Singular Value Thresholding (RTSVT) operator to lower the computational cost. In addition, in contrast with most MM approaches, our algorithm is guaranteed to converge to the stationary points. Experimental results on synthetic data, image inpainting show that the completion results exceed or achieve comparable performance than state-of-the-art, yet several times faster.
       
  • Sketch simplification based on conditional random field and least squares
           generative adversarial networks
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Qianwen Lu, Qingchuan Tao, Yalin Zhao, Manxiao LiuAbstractSketch simplification is a critical part of cartoon drawing work. Some existing approaches are already capable of simplifying simple sketches, but in some cases, they are still insufficient because of method diversity of sketch drawing and complexity of sketch content. In this paper, we present a novel approach of building the model for sketch simplification, which is based on the conditional random field (CRF) and Least Squares generative adversarial networks (LSGAN). Through the zero-sum game of the generator and the discriminator in the model and the restriction of the conditional random field, the model can generate the simplified images, which are more similar to standard line images. The dataset we build contains a large number of image pairs that are drawn in different painting ways and with different contents. Finally, experiments show that our approach can obtain better results than the state of the art approaches in sketch simplification.
       
  • Pedestrian recognition in multi-camera networks based on deep transfer
           learning and feature visualization
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Jing-Tao Wang, Guo-Li Yan, Hui-Yan Wang, Jing HuaAbstractThe extensive deployment of surveillance cameras in public places, such as subway stations and shopping malls, necessitates automated visual-data processing approaches to match pedestrians across non-overlapping multiple cameras. However, due to the insufficient number of labeled training samples in real surveillance scene, it is difficult to train an effective deep neural network for cross-camera pedestrian recognition. Moreover, the cross-camera variation in viewpoint, illumination, and background makes the task even more challenging. To address these issues, in this paper we propose to transfer the parameters of a pre-trained network to our target network and then update the parameters adaptively using training samples from the target domain. More importantly, we develop new network structures that are specially tailored for cross-camera pedestrian recognition task, and implement a simple yet effective multi-level feature fusion method that yield more discriminative and robust features for pedestrian recognition. Specifically, rather than conventionally perform classification on the single-level feature of the last feature layer, we instead utilize multi-level feature by associating feature visualization with multi-level feature fusion. As another contribution, we have published our codes and extracted features to facilitate further research. Extensive experiments are conducted on WARD, PRID and MARS datasets, we show that the proposed method consistently outperforms state-of-the-arts.
       
  • Sparse dual graph-regularized NMF for image co-clustering
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Jing Sun, Zhihui Wang, Fuming Sun, Haojie LiAbstractNonnegative matrix factorization (NMF) as fundamental technique for clustering has been receiving more and more attention. This is because it can effectively reduce high dimensional data and produce parts-based, linear image representations of nonnegative data. For practical clustering tasks, NMF ignores the geometric structures of both data manifold and feature manifold. In addition, recent research results showed that leveraging sparseness can greatly improve the ability of learning parts. Motivated by the two aspects above mentioned, we propose a novel co-clustering algorithm to enhance the clustering performance, called sparse dual graph-regularized nonnegative matrix factorization (SDGNMF). It aims for finding a parts-based, linear representation of the non-negative data and facilitating the learning tasks. SDGNMF jointly incorporates the dual graph-regularized and sparseness constraints as additional conditions to uncover the intrinsic geometrical, discriminative structures of the data space and feature space. The iterative updating scheme for the optimization problem of SDGNMF and its convergence proofs are also given in detail. Experimental results of clustering on three benchmark datasets demonstrated that SDGNMF algorithm outperforms the compared state-of-the-art methods in image co-clustering.
       
  • SliceNet: A proficient model for real-time 3D shape-based recognition
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Xuzhan Chen, Youping Chen, Kashish Gupta, Jie Zhou, Homayoun NajjaranAbstractThe field of 3D object recognition has been dominated by 2D view-based methods mostly because of lower accuracy and larger computational load of 3D shape-based methods. Recognition with a 3D shape yields appreciable advantages e.g., making use of depth information and independence to ambient lighting, but we are still away from an eminent solution for 3D shape-based object recognition. In this paper first, a statistical method capable of modeling the input and output with random variables is used to investigate the reasons contributing to the inferior performance of the 3D convolution operation. The analysis suggests that the excessive size of the kernel causes the dramatic blowing up of the output variance of the 3D convolution operation and makes the output feature less discriminating. Then, based on the results of this analysis and inspired by the underlying principle of 3D shapes, SliceNet is proposed to learn 3D shape features using anisotropic 3D convolution. Specifically, the proposed method learns features from original 2D planar sketches comprising the 3D shape and has a significantly lower output variance. Experiments on ModelNet show that the recognition accuracy of the proposed SliceNet is comparable to well-established 2D view-based methods. Besides, the SliceNet also has a significantly smaller model size, simpler architecture, less training and inference time compared to 2D view-based and other 3D object recognition methods. An experiment with real-world data shows that the model trained on CAD files can be generalized to real-world objects without any re-training or fine-tuning.
       
  • Learning discriminative visual elements using part-based convolutional
           neural network
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Lingxiao Yang, Xiaohua Xie, Jianhuang LaiAbstractMid-level element based representations have been proven to be very effective for visual recognition. This paper presents a method to discover discriminative mid-level visual elements based on deep Convolutional Neural Networks (CNNs). We present a part-level CNN architecture, namely Part-based CNN (P-CNN), which acts as a role of encoding module in a part-based representation model. The P-CNN can be attached at arbitrary layer of a pre-trained CNN and be trained using image-level labels. The training of P-CNN essentially corresponds to the optimization and selection of discriminative mid-level visual elements. For an input image, the output of P-CNN is naturally the part-based coding and can be directly used for image recognition. By applying P-CNN to multiple layers of a pre-trained CNN, more diverse visual elements can be obtained for visual recognitions. We validate the proposed P-CNN on several visual recognition tasks, including scene categorization, action classification and multi-label object recognition. Extensive experiments demonstrate the competitive performance of P-CNN in comparison with state-of-the-arts.
       
  • Hybrid GNN-ZNN models for solving linear matrix equations
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Predrag S. Stanimirović, Vasilios N. Katsikis, Shuai LiAbstractNew dynamical models for solving the matrix equations BX=D and XC=D are developed in time-invariant case. These models are derived as a combination of GNN and ZNN models. They do not posses GNN dynamic due to their implicit dynamics. Formally observed, they can be derived by multiplying the right hand side in the ZNN dynamics by an appropriate symmetric positive definite matrix which improves the convergence rate. For this purpose, these models are termed as HZNN. The convergence of HZNN models is global and exponential. Also, the convergence rate of HZNN models is superior with respect to the convergence rate of the classical GNN model as well as with respect to ZNN models in time-invariant case. Capability of the HZNN models to overcome unavoidable implementation noises is considered theoretically and numerically. The Matlab implementation of HZNN models is proposed and used in numerical experiments for solving matrix equations and computing various appearances of outer inverses with prescribed range and null space.
       
  • Unpaired cross domain image translation with augmented auxiliary domain
           information
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Yan Gan, Junxin Gong, Mao Ye, Yang Qian, Kedi LiuAbstractImage translation is that converting an image from one domain to another domain. Many existing methods with GANs learn a mapping function by adversarial loss and other constraints. However, this learned mapping function can not express the detailed information of generated images and its generalization capability is not enough. To address this problem, in this paper, we propose an unpaired generative adversarial networks model with augmented auxiliary domain. The proposed model combines augmented auxiliary domain with the domains to be learned together to model. In particular, we design multiple generators and discriminators to achieve unpaired cross domain learn. The designed generators and discriminators are subject to multiple adversarial losses and full cycle constraint losses, which can learn the information of augmented auxiliary domain and reduce their mapping space. At last, we conduct experiments on seven cases and the results show that our model has better performance than other unpaired cross domain methods.
       
  • On the asynchronous bipartite consensus for discrete-time second-order
           multi-agent systems with switching topologies
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Jinliang Shao, Lei Shi, Yangzhen Zhang, Yuhua ChengAbstractThe asynchronous bipartite consensus for a group of agents with second-order dynamics is examined in this paper, where the asynchrony means that the time instants when each agent receives the neighbors’ data information are completely independent of other agents’. The communication among the agents is described by a time-varying signed and structurally balanced digraph, which is equivalent to assuming that the agents can be divided into two groups without any common agents, in which the agents within the same group are cooperative and the agents between different groups are competitive. An asynchronous distributed control protocol is designed to implement the bipartite consensus. By using the product properties of row-stochastic matrices from a noncompact set, a sufficient condition can be established under a loose assumption that is the union of communication topologies related to any time intervals with given length has a spanning tree. Finally, a simulation instance is provided to verify the reachability of asynchronous bipartite consensus.
       
  • Multi-scale deep encoder-decoder network for salient object detection
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Qinghua Ren, Renjie HuAbstractDeep convolutional neural networks (CNNs) have recently made revolutionary improvements in salient object detection. However, most existing CNN-based models fail to precisely separate the whole salient object(s) from a cluttered background due to the downsampling effects or the patch-level operation. In this paper, we propose a multi-scale deep encoder-decoder network which learns discriminative saliency cues and computes confidence scores in an end-to-end fashion. The encoder network extracts meaningful and informative features in a global view, and the decoder network recovers lost detailed object structure in a local perspective. By taking multiple resized images as the inputs, the proposed model incorporates multi-scale features from a shared network and predicts a fine-grained saliency map at the pixel level. To easily and efficiently train the whole network, the light-weighted decoder breaks through the limit of conventional symmetric structure. In addition, a two-stage training strategy is designed to encourage the robustness and accuracy of the network. Without any post-processing steps, our method is capable of significantly reducing the computation complexity while densely segmenting foreground objects from an image. Extensive experiments on six challenging datasets demonstrate that the proposed model outperforms other state-of-the-art approaches in terms of various evaluation metrics.
       
  • Three-dimensional piecewise cloud representation for time series data
           mining
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Gangquan Si, Kai Zheng, Zhou Zhou, Chengjie Pan, Xiang Xu, Kai Qu, Yanbin ZhangAbstractMany researchers have taken interests in time series data mining to discover potential knowledge and information as the amount of data from various domains rapidly increases. Representation, as a necessary implementation component of data mining, is critical to reduce the high dimensionality of time series data and generate a corresponding distance measure to process time series data effectively and efficiently. Many high-level representation approaches for mining time series data have been proposed in the past decades, e.g., PAA, SAX, PWCA and 2D-NCR. In this paper, a novel representation method for time series data, which is named Three-Dimensional Piecewise Cloud Representation (TDPCR), is proposed. The new representation contains a flexible partitioning strategy which protects the connection information between consecutive points by overlapping two adjacent segments. Using the improved cloud model theory, the proposed representation achieves the reduction of the data dimensionality and captures distribution and variation features of segments. Furthermore, a new distance measure, which has adaptive weight factors to adjust the proportion of data information, is defined to describe the relationship between two three-dimensional clouds. Accompanied with the comparisons of state-of-the-art representation methods, a sufficient performance evaluation for the proposed representation is carried out in the classification and query by content tasks. The experimental results show that TDPCR is effective and competitive on most of datasets from several domains.
       
  • Spatial-spectral classification of hyperspectral image via group tensor
           decomposition
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Guangzhe Zhao, Bing Tu, Hongyan Fei, Nanying Li, Xianchang YangAbstractIn this paper, a novel group tensor decomposition (GTD) method is proposed to alleviate within-class spectral variation by fully exploit the low-rank property of 3D HSI, which can significantly improve the classification performance. Specifically, the spectral dimension of the HSI is firstly reduced with principal component analysis (PCA) algorithm. Then, the dimension reduced image is segmented into a set of overlapping 3D tensor patches, which are then clustered into groups by K-means algorithm. By unfolding the similar tensors of each group into a set of matrices and stacking them, these similar tensor patches are constructed as a new tensor. Next, the intrinsic spectra tensor and its corresponding spectral variation tensor of each new tensor are estimated with a low-rank tensor decomposition (LRTD) algorithm. By aggregating all intrinsic spectra tensor in each group, we can obtain an integral intrinsic spectra tensor and separate its corresponding spectral variation tensor. Finally, the pixel-wise classification is performed only on the intrinsic spectra tensor, which can reflect the material-dependent properties of different objects. Experimental results on real HSI data sets demonstrate the superiority of the proposed GTD algorithm over several well-known classification approaches.
       
  • Neural adaptive tracking control for a class of high-order non-strict
           feedback nonlinear multi-agent systems
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Yun Shang, Bing Chen, Chong LinAbstractThis research is mainly concerned with the consensus tracking problem for a class of high-order non-strict feedback nonlinear multi-agent systems, in which the virtual and the actual control items of each follower dynamics are the power functions with positive odd integers rather than linear items. Due to this structural feature of the high-order systems, adding a power integrator technique is employed in controller design to overcome the obstacle caused by high power of virtual and real control items. Meanwhile, Radial Basis Function Neural Networks are used to approximate the uncertain nonlinearities. An adaptive tracking strategy is proposed for this type of multi-agent systems. It is shown that the suggested control scheme can guarantee the boundedness of all the closed-loop signals and ensure that all outputs of follower agents track the leader signal synchronously. Since high-order non-strict feedback nonlinear multi-agent systems include some existing nonlinear multi-agent systems as the special case, our result can be used to control more general nonlinear multi-agent systems. Finally, a numerical example is presented to further verify the effectiveness of the proposed algorithm.
       
  • Cross-covariance regularized autoencoders for nonredundant sparse feature
           representation
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Jie Chen, ZhongCheng Wu, Jun Zhang, Fang Li, WenJing Li, ZiHeng WuAbstractWe propose a new feature representation algorithm using cross-covariance in the context of deep learning. Existing feature representation algorithms based on the sparse autoencoder and nonnegativity-constrained autoencoder tend to produce duplicative encoding and decoding receptive fields, which leads to feature redundancy and overfitting. We propose using the cross-covariance to regularize the feature weight vector to construct a new objective function to eliminate feature redundancy and reduce overfitting. The results from the MNIST handwritten digits dataset, the NORB normalized-uniform dataset and the Yale face dataset indicate that relative to other algorithms based on the conventional sparse autoencoder and nonnegativity-constrained autoencoder, our method can effectively eliminate feature redundancy, extract more distinctive features, and improve sparsity and reconstruction quality. Furthermore, this method improves the image classification performance and reduces the overfitting of conventional networks without adding more computational time.
       
  • Correntropy based graph regularized concept factorization for clustering
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Siyuan Peng, Wee Ser, Badong Chen, Lei Sun, Zhiping LinAbstractConcept factorization (CF) technique is one of the most powerful approaches for feature learning, and has been successfully adopted in a wide range of practical applications such as data mining, computer vision, and information retrieval. Most existing concept factorization methods mainly minimize the square of the Euclidean distance, which is seriously sensitive to non-Gaussian noises or outliers in the data. To alleviate the adverse influence of this limitation, in this paper, a robust graph regularized concept factorization method, called correntropy based graph regularized concept factorization (GCCF), is proposed for clustering tasks. Specifically, based on the maximum correntropy criterion (MCC), GCCF is derived by incorporating the graph structure information into our proposed objective function. A half-quadratic optimization technique is adopted to solve the non-convex objective function of the GCCF method effectively. In addition, algorithm analysis of GCCF is studied. Extensive experiments on real world datasets demonstrate that the proposed GCCF method outperforms seven competing methods for clustering applications.
       
  • Storage capacity of rotor Hopfield neural networks
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Masaki KobayashiAbstractHopfield neural networks have been studied by many researchers. A complex-valued Hopfield neural network (CHNN) is a multistate model of Hopfield neural network, and has been applied to the storage of multilevel data, such as image data. A rotor Hopfield neural network (RHNN) is an extension of CHNN. The RHNNs demonstrated double the storage capacity of CHNNs and excellent noise tolerance by computer simulations. Jankowski et al. analyzed the storage capacity of CHNNs by approximating the crosstalk term using central limit theorem. In this work, we show that the RHNNs have double the storage capacity of the CHNNs based on their theory.
       
  • Stability analysis on state-dependent impulsive Hopfield neural networks
           via fixed-time impulsive comparison system method
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Yinghua Zhou, Chuandong Li, Hui WangAbstractThis paper aims at the stability problem of state-dependent impulsive Hopfield neural networks. Under well-selected conditions, we transform the considered neural networks into the analog with fixed-time impulses, namely, fixed-time impulsive comparison systems. By means of the stability theory of fixed-time impulsive systems, we establish several sufficient conditions for the exponential stability of state-dependent impulsive Hopfield neural networks. The present results show that state-dependent impulsive Hopfield neural networks can remain stability property of continuous subsystem even if the impulses are of somewhat destabilizing, and that stabilizing impulses can stabilize the unstable continuous subsystem. We illustrate the validity of the theoretical results by three numerical examples.
       
  • Distributed coordination of multiple mobile actuators for pollution
           neutralization
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Kai Luo, Ming Chi, Jie Chen, Zhi-Hong Guan, Chang-Xin Cai, Ding-Xue ZhangAbstractThis paper is concerned with distributed coordination of multiple mobile actuators for pollution neutralization in a polluted environment, where a static mesh sensor network is pre-deployed for measuring the concentration of contaminants, and mobile actuators with neutralizing chemicals implement spraying operation at a steady rate to reduce the contaminants continuously. A hazard intensity distribution is introduced to evaluate adverse impact of contaminants on the environment. Autonomous actuators are dynamically deployed to minimize the total hazard intensity. This coordination problem can be formulated as a distributed deployment problem based on centroidal Voronoi tessellation (CVT). Two control strategies with switching motion controllers are proposed to achieve optimal deployment of mobile actuators for unlimited and limited actuating range respectively. To escape local minimum and balance the actuator workload, a novel workload adjustment strategy is designed to change the normalized amount of neutralizer sprayed by mobile actuators, which makes each actuator approach a common workload. Compared with pure CVT and switching motion controller, the total hazard intensity can be further decreased if the workload adjustment strategy is implemented. Simulation examples are provided to validate the effectiveness of the proposed method.
       
  • An extended dictionary representation approach with deep subspace learning
           for facial expression recognition
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Zhe Sun, Raymond Chiong, Zheng-ping HuAbstractDeep subspace learning (DSL) models based on the principal component analysis network (PCANet) and linear discriminant analysis network (LDANet) have shown to be promising alternatives to deep learning models when there are computing power and training data constraints. However, high dimensionality of the feature space remains a major issue for DSL models. This paper presents a novel DSL approach based on an extended dictionary representation with deep subspace features for facial expression recognition. First, we propose the use of feature pooling with DSL by adding rank-based average pooling between each subspace mapping layer. We then use spatial pyramid pooling in the output layer to overcome the high-dimensionality problem. After that, the extended dictionary is formed by expanding the feature dictionary. Finally, we apply sparse representation classification with squared ℓ2-regularization to improve the recognition accuracy. Comprehensive experiments based on several well-established datasets confirm that our proposed approach has superior performance compared to both the baseline as well as state-of-the-art PCANet and LDANet methods, not just in terms of accuracy but also robustness against block occlusion and random corruption.
       
  • Extended adaptive event-triggered formation tracking control of a class of
           multi-agent systems with time-varying delay
    • Abstract: Publication date: 17 November 2018Source: Neurocomputing, Volume 316Author(s): Tao Li, Zhipeng Li, Shaobo Shen, Shumin FeiAbstractThis paper considers the adaptive event-triggered formation tracking control of a class of second-order multi-agent systems (MASs) with time-varying delay. Firstly, a distributed control protocol is constructed and some extended adaptive event-triggered schemes are presented, where the triggering thresholds rely on the real-time variation of the MASs but not not pre-selected constants. Then by choosing an augmented Lyapunov–Krasovskii functional (LKF), two delay-dependent criteria are formulated in terms of linear matrix inequalities (LMIs). Especially, during estimating the upper bound on LKF’s derivative, since some novel integral inequalities and a new convex technique are utilized, the conservatism can be greatly reduced. Finally, two numerical examples with some simulations are provided to illustrate our methods.
       
  • Adaptive Neural-Network-Based Tracking Control Strategy of Nonlinear
           Switched Non-Lower Triangular Systems with Unmodeled Dynamics
    • Abstract: Publication date: Available online 11 September 2018Source: NeurocomputingAuthor(s): Wanlu Zhou, Ben Niu, Xuejun Xie, Fuad E. AlsaadiAbstractIn this paper, an adaptive neural-network-based tracking control strategy is proposed for a class of nonlinear switched non-lower triangular systems with the completely unknown nonlinearities and unmodeled dynamics. The design difficulties arise mainly from the fact that the intercoupling between the non-lower triangular functions and the unmodeled dynamics leads the switched system under consideration has a very complex structure. In order to get the desired adaptive state feedback controller, a mild assumption associated with a dynamic signal is utilized to deal with the unmodeled dynamics, and a separating variable method is presented to handle the system nonlinearities with all state variables in the framework of adaptive backstepping technique, respectively. The obtained results show that all signals of the switched closed-loop system are semi-global bounded with the output tracking error can be guaranteed to enter a small region around the origin. In the end, two simulation examples are given to demonstrate the feasibility and practicability of the presented design strategy.
       
  • Modified Frank–Wolfe Algorithm for Enhanced Sparsity in Support
           Vector Machine Classifiers
    • Abstract: Publication date: Available online 9 September 2018Source: NeurocomputingAuthor(s): Carlos M. Alaíz, Johan A.K. SuykensAbstractThis work proposes a new algorithm for training a re-weighted ℓ2 Support Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Candès et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In particular, the margin required for each training vector is set independently, defining a new weighted SVM model. These weights are selected to be binary, and they are automatically adapted during the training of the model, resulting in a variation of the Frank–Wolfe optimization algorithm with essentially the same computational complexity as the original algorithm.As shown experimentally, this algorithm is computationally cheaper to apply since it requires less iterations to converge, and it produces models with a sparser representation in terms of support vectors and which are more stable with respect to the selection of the regularization hyper-parameter.
       
  • Image captioning with Triple-Attention and Stack Parallel LSTM
    • Abstract: Publication date: Available online 4 September 2018Source: NeurocomputingAuthor(s): Xinxin Zhu, Lixiang Li, Jing Liu, Ziyi Li, Haipeng Peng, Xinxin NiuAbstractImage captioning aims to describe the content of images with a sentence. It is a natural way for people to express their understanding, but a challenging and important task from the view of image understanding. In this paper, we propose two innovations to improve the performance of such a sequence learning problem. First, we give a new attention method named triple attention (TA-LSTM) which can leverage the image context information at every stage of LSTM. Then, we redesign the structure of basic LSTM, in which not only the stacked LSTM but also the paralleled LSTM are adopted, called as PS-LSTM. In this structure, we not only use the stack LSTM but also use the parallel LSTM to achieve the improvement of the performance compared with the normal LSTM. Through this structure, the proposed model can ensemble more parameters on single model and has ensemble ability itself. Through numerical experiments, on the public available MSCOCO dataset, our final TA-PS-LSTM model achieves comparable performance with some state-of-the-art methods.
       
  • Deep Saliency Detection via Channel-Wise Hierarchical Feature Responses
    • Abstract: Publication date: Available online 7 September 2018Source: NeurocomputingAuthor(s): Cuiping Li, Zhenxue Chen, Q. M. Jonathan Wu, Chengyun LiuAbstractRecently, deep learning-based saliency detection has achieved fantastic performance over conventional works. In this paper, we pay close attention to channel-wise feature responses and propose an end-to-end deep learning-based saliency detection method. Our model contains three main components, which are channel-wise coarse feature extraction, channel-wise hierarchical feature refinement (CHFR), and hierarchical feature maps fusion. The whole process is based on the squeeze-and-excitation-residual network (SE-ResNet) to explicitly and globally model the inter dependencies between the channels of its convolution features at slightly computational cost. We first make channel-wise feature extraction to produce coarse feature maps with much information loss. To make full use of spatial information and fine details, CHFR is executed based on SE-ResNet modules to make hierarchical feature refinement. After that, the hierarchical feature maps are fused to generate the final saliency map. Compared with other fifteen state-of-the-art approaches, the experimental results demonstrate the high computational efficiency and superior performance of the proposed approach according to comprehensive evaluations over six benchmark datasets.
       
  • Couple-Group Consensus for Discrete-Time Heterogeneous Multiagent Systems
           with Cooperative-Competitive Interactions and Time Delays
    • Abstract: Publication date: Available online 6 September 2018Source: NeurocomputingAuthor(s): Yiliu Jiang, Lianghao Ji, Qun Liu, Shasha Yang, Xiaofeng LiaoAbstractWe investigate couple-group consensus problems for a class of discrete-time heterogeneous systems consisting of first-order and second-order agents under the influence of communication and input time delays. A novel consensus protocol is designed by utilizing cooperative and competitive interactions among agents, so that we solve the couple-group consensus problems casting off a conservation condition of in-degree balance which widely exists in relevant articles. Based on frequency-domain analysis and matrix theory, some sufficient conditions are derived to ensure the achievement of group consensus in some cases, and the upper bound of input time delays are consequently estimated. The results show that the achievement of couple-group consensus is closely relate to the coupling weights between the agents, the control parameters of the systems, the sampled information of discrete-time systems as well as the agents’ input time delays, while it is irrelevant to communication delays. Finally, we provide several simulations to illustrate the correctness of the theoretical results.
       
  • A New Switching-Delayed-PSO-Based Optimized SVM Algorithm for Diagnosis of
           Alzheimer’s Disease
    • Abstract: Publication date: Available online 6 September 2018Source: NeurocomputingAuthor(s): Nianyin Zeng, Hong Qiu, Zidong Wang, Weibo Liu, Hong Zhang, Yurong LiAbstractIn healthcare sector, it is of crucial importance to accurately diagnose Alzheimer’s disease (AD) and its prophase called mild cognitive impairment (MCI) so as to prevent degeneration and provide early treatment for AD patients. In this paper, a framework is proposed for the diagnosis of AD, which consists of MRI images preprocessing, feature extraction, principal component analysis, and the support vector machine (SVM) model. In particular, a new switching delayed particle swarm optimization (SDPSO) algorithm is proposed to optimize the SVM parameters. The developed framework based on the SDPSO-SVM model is successfully applied to the classification of AD and MCI using MRI scans from ADNI dataset. Our developed algorithm can achieve excellent classification accuracies for 6 typical cases. Furthermore, experiment results demonstrate that the proposed algorithm outperforms several SVM models and also two other state-of-art methods with deep learning embedded, thereby serving as an effective AD diagnosis method.
       
  • Coevolutionary multi-task learning for feature-based modular pattern
           classification
    • Abstract: Publication date: Available online 6 September 2018Source: NeurocomputingAuthor(s): Rohitash Chandra, Sally CrippsAbstractDue to modular representation in biological neural systems, the absence of certain single sensory inputs does not hinder the decision-making process. For instance, the damage to an eye does not result in the loss of the entire sight. In previous work, coevolutionary multi-task learning was presented that featured a synergy between multi-task learning and coevolutionary algorithms. In this paper, we extend it for robust decision making in pattern classification problems given incomplete information. The method trains a cascaded neural network architecture to autonomously address the absence of certain input features and disruptions to neural connections. The results show that the method is comparable to conventional learning methods while having the advantage of providing decision given incomplete information. Moreover, it provides a way for developmental learning while providing the means to quantify feature contribution.
       
  • Trace Ratio Criterion based Discriminative Feature Selection via l 2,
           p -norm Regularization for Supervised Learning
    • Abstract: Publication date: Available online 5 September 2018Source: NeurocomputingAuthor(s): Mingbo Zhao, Mingquan Lin, Chi-Yuen Bernard Chiu, Zhao Zhang, Xuesong TangAbstractDealing with high-dimensional dataset has always been an important problem and feature selection is one of useful tools. In this paper, we develop a new filter based supervised feature selection method by combining Trace Ratio Criterion of Linear Discriminant Analysis (TRC-LDA) and group sparsity regularization. The filter based supervised feature selection method is a classifier-independent method while the TRC-LDA criterion is a recently developed criterion for dimensionality reduction that can well preserve discriminative information of dataset. However, there are seldom methods by utilizing TRC-LDA criterion for feature selection. On the other hand, imposing the l2,0-norm to the projection matrix of TRC-LDA will force some rows in it to be zero while keep other rows nonzero making the index of nonzero rows to be the selected features, however, l2, 0-nom minimizing problem is NP-hard and intractable. To solve the above problem, in this paper, we develop a new method, namely, Trace Ratio Criterion Discriminative Feature Selection (TRC-DFS), for feature selection. The proposed TRC-DFS has imposed l2, 1-norm, i.e. an approximation of l2, 0-norm, to the projection matrix W of TRC-LDA to achieve feature selection. As a result, the proposed TRC-DFS can both achieve feature selection as well as capture the discriminative structure of data. We also extend the proposed method with l2, p-norm (0 
       
  • Synchronization-based Passivity of Partially Coupled Neural Networks with
           Event-triggered Communication
    • Abstract: Publication date: Available online 5 September 2018Source: NeurocomputingAuthor(s): Chi Huang, Wei Wang, Jinde Cao, Jianquan LuAbstractIn this paper, synchronization-based passivity of coupled neural networks (CNNs) with partial and event-triggered communication is discussed. Event conditions are designed based on the partial couplings among neural networks. A regrouping method is introduced to build a channel Laplacian matrix which contains the structural information of both couplings and channels. Based on such new matrix, a novel error system is established for the purpose of synchronizing the CNNs. A sufficient condition for solving the synchronization problem of partially coupled neural networks (PCNNs) is given. Moreover, the same condition can also verify the passivity of networks when noise is nonzero. Finally, a numerical example demonstrates the effectiveness of the control mechanism.
       
  • Sliding mode control for networked systems with randomly varying
           nonlinearities and stochastic communication delays under uncertain
           occurrence probabilities
    • Abstract: Publication date: Available online 5 September 2018Source: NeurocomputingAuthor(s): Panpan Zhang, Jun Hu, Hongjian Liu, Changlu ZhangAbstractIn this paper, we aim to propose the robust sliding mode control (SMC) scheme for discrete networked systems subject to randomly occurring uncertainty (ROU), randomly varying nonlinearities (RVNs) and multiple stochastic communication delays (MSCDs). Here, a series of mutually independent Bernoulli distributed random variables is introduced to model the phenomena of the ROU, RVNs and MSCDs, where the occurrence probabilities of above phenomena are allowed to be uncertain. For the addressed systems, an SMC strategy is given such that, for above network-induced phenomena, the stability of the resulted sliding motion can be guaranteed by presenting a new delay-dependent sufficient criterion via the delay-fractioning method. Moreover, the discrete sliding mode controller is synthesized such that the state trajectories of the system are driven onto a neighborhood of the specified sliding surface and remained thereafter, i.e., the reachability condition in discrete-time setting is verified. Finally, the usefulness of the proposed SMC method is illustrated by utilizing a numerical example.
       
  • A Granular Functional Network with delay: some dynamical properties and
           application to the sign prediction in social networks
    • Abstract: Publication date: Available online 4 September 2018Source: NeurocomputingAuthor(s): Vincenzo Loia, Domenico Parente, Witold Pedrycz, Stefania TomasielloAbstractIn this paper, we propose a general scheme of Functional Network, by considering granularity of information and time delay. Functional Networks (FNs) are a relatively recent alternative to standard Neural Networks (NNs). They have shown better performance in comparison to performance of NNs. Data granulation used in the development of NNs allows for the formation of more efficient and transparent architectures. Time delay models have been recognized to be more realistic constructs of real-world systems. By keeping these observations in mind, we revise the usual design scheme of FN by casting it in the settings of information granules, defining a different learning algorithm, and by introducing time delay. Under some assumptions, we discuss some dynamical properties of the proposed model, in particular those concerning asymptotic stability and Neimark–Sacker bifurcation. Finally, we present an application of the proposed method to the problem of sign prediction in social networks. The results reported against those obtained by the state-of-the-art method show good performance of the proposed approach.
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.80.58.121
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-