The Principle of Homology Continuity and Geometrical Covering Learning for Pattern Recognition

Homology Continuity is a fundamental property of the nature, but few of the traditional pattern recognition algorithms were aware of it. Firstly, this paper gives a brief description to the Principle of Homology Continuity (PHC), and tries to mathematically redefine it. Then, we introduce a PHC-based pattern learning method - Geometrical Covering Learning (GCL), following the Hyper sausage neural network as an instance of GCL. Lastly, we propose a GCL solution to the two-spirals pattern recognition problem. The final experimental results show that the new method is feasible and efficient.