Feature Discovery by Enhancement and Relaxation of Competitive Units

Authors:
Ryotaro Kamimura
Affiliations:
IT Education Center, Tokai University, Kanagawa, Japan 259-1292
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

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Abstract

In this paper, we introduce a new concept of enhancement and relaxation to discover features in input patterns in competitive learning. We have introduced mutual information to realize competitive processes. Because mutual information is an average over all input patterns and competitive units, it cannot be used to detect detailed feature extraction. To examine in more detail how a network is organized, we introduce the enhancement and relaxation of competitive units through some elements in a network. With this procedure, we can estimate how the elements are organized with more detail. We applied the method to a simple artificial data and the famous Iris problem to show how well the method can extract the main features in input patterns. Experimental results showed that the method could more explicitly extract the main features in input patterns than the conventional techniques of the SOM.