Topological Higher-Order Neuron Model Based on Homology-Continuity Principle

Abstract

Recently, deep neural networks (DNNs) have been applied in various areas in computer vision. However, due to the simple neuron configuration, the existing DNNs rely heavily on the diversity of the data distribution and have poor generalization performance. In addition, the excessive network complexity also leads to low learning efficiency. To solve above problems, a topological higher-order neuron model was proposed, which is modeled according to the point-set topology in high-dimensional space. The proposed neuron is determined by the adaptively changed center and the direction weights with strong generalization ability. The high-dimensional feature space of the samples is constructed by a category-based covering learning method, thus achieving an optimal coverage of each category. Experiments on several different classical datasets demonstrate the generalization of the proposed neuron model, which provides a new approach for the further development of DNNs and can be widely used in computer vision.