Research on multi-degree-of-freedom neurons with weighted graphs

Authors:
Shoujue Wang;Singsing Liu;Wenming Cao
Affiliations:
Artificial Neural Networks Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, P.R. China;Artificial Neural Networks Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, P.R. China;Institute of Intelligent Information System, Information Engineering College, Zhejiang University of Technology, Hangzhou, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model — Multi-Degree-of-Freedom Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points set’s topological character in the feature space, which is different from the traditional “separation” method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.