A Biomimetic Covering Learning Method Based on Principle of Homology Continuity

Xin Ning; Yuebao Wang; Weijuan Tian; Liang Liu; Weiwei Cai

doi:10.58682/TGRS.2024.8888888

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2023, Volume 1, Issue 1: 1-13. DOI: 10.58682/TGRS.2024.8888888

Open Access | Research Article | Feature Paper | 10 November 2023

A Biomimetic Covering Learning Method Based on Principle of Homology Continuity

Xin Ning 1

Yuebao Wang 2 *

Weijuan Tian 2

Liang Liu 2

Weiwei Cai 3

1 Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China

2 Cognitive Computing Technology Joint Laboratory, Wave Group, Beijing 10008, China

3 Graduate School, Northern Arizona University, Flagstaff, AZ 86011, USA

* Corresponding Author

Received: 10 November 2023, Accepted: 10 November 2023, Published: 10 November 2023

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.

Graphical Abstract

Keywords

Brain-inspired

Covering neuron models

Deep learning

Homology continuity

Optimal coverage

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