Information enhancement for interpreting competitive learning
Ryotaro Kamimura · International Journal of General Systems · 2010
In this paper, we propose a new method called information enhancement to interpret internal representations of competitive learning. We consider competitive learning as a process of mutual information maximisation on input patterns. Then, we examine to what extent this mutual information can be increased or decreased by focusing upon or enhancing some elements in a network. If this enhancement for the elements increases information on input patterns, these elements possess more information on input patterns. Thus, we only have to carefully examine those elements in a network. We applied the method to an artificial problem, the Iris problem and an air pollution problem. In all problems, we succeeded in extracting important features in patterns. In addition, final maps were better than those obtained by the conventional self-organising map. We can say that this is the first step towards the full understanding of internal representations in competitive learning.