A Biomimetic Covering Learning Method Based on Principle of Homology Continuity

Authors

DOI

https://doi.org/10.52810/TPRIS.2021.100009

Keywords:

Brain-inspired, covering neuron models, deep learning, homology continuity, optimal coverage

Abstract

The Principle of Homology Continuity (PHC) based covering learning method is an effective method to solve the pattern recognition problem. However, PHC and the existence of optimal coverage are not mathematical proven. To address this issue, we firstly give the mathematical description and theoretical proof of PHC. On this basis, the theoretical definition of optimal coverage is introduced. Optimal coverage can determine the internal connections among samples as prior knowledge and use covering neurons to learn prior knowledge. Finally, we propose a kind of covering neuron model, and the effectiveness of which is demonstrated through extensive experiments conducted on the CIFAR-10, LFW, and YTF datasets.

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Author Biographies

Xin Ning, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China

Xin Ning received the B.S. degree in software engineering from Xinjiang University, Wulumuqi, China, in 2012, and the Ph.D. degree in electronic circuit and system from the Institute of Semiconductors, Chinese Academy of Sciences, in 2017. He is currently an Associate Professor with the Laboratory of Artifificial Neural Networks and High Speed Circuits, Institute of Semiconductors, Chinese Academy of Sciences. He has authored or co-authored more than 40 papers in journals and refereed conferences. His current research interests include pattern recognition, computer vision, and image processing.

Yuebao Wang, Cognitive Computing Technology Joint Laboratory, Wave Group, Beijing 10008, China

Yuebao Wang received a B.S Degree in mathematics and Applied Mathematics   from North University of China in 2013, and  received M.S degree in operations research and cybernetics  Currently in 2018. He worked as an algorithm research fellow in the Cognitive Computing Technology Joint Laboratory of Wave Group. His current research interests include machine learning, function approximation theory and TDA.

Weijuan Tian, Cognitive Computing Technology Joint Laboratory, Wave Group, Beijing 10008, China

Weijuan Tian received her M.S. degrees from School of Electronic Engineering in Xidian University in 2017. Currently, she worked as an algorithm research fellow in the Cognitive Computing Technology Joint Laboratory of Wave Group. Her research interests include machine learning, DNNs, neuron modeling, video and image processing.

Liang Liu, Cognitive Computing Technology Joint Laboratory, Wave Group, Beijing 10008, China

Liang Liu Received dual B.S. degrees from Wuhan University and Wuhan University of Technology, and the Ph.D. degree in North China Electric Power University. He worked as an algorithm research fellow in the Cognitive Computing Technology Joint Laboratory of Wave Group. His current research interests include DNNs, computer vision, and image processing.

Weiwei Cai, Central South University of Forestry and Technology, Changsha 410004, China

Weiwei Cai is currently pursuing the master’s degree with the Central South University of Forestry and Technology, Changsha, China. Prior to that, he worked in IT industry for more than ten years in the roles of an Enterprise Architect and a Program Manager. His research interests include machine learning, deep learning, and computer vision.

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A Biomimetic Covering Learning Method Based on Principle of Homology Continuity

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Published

2021-04-22

How to Cite

Ning, X., Wang, Y., Tian, W., Liu, L., & Cai, W. (2021). A Biomimetic Covering Learning Method Based on Principle of Homology Continuity. ASP Transactions on Pattern Recognition and Intelligent Systems, 1(1), 9–16. https://doi.org/10.52810/TPRIS.2021.100009

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Section

Regular Paper