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Flexible self-organizing maps by information maximization

In this paper, we propose a new information theoretic method for self-organizing maps. The method aims to control competitive processes flexibly, that is, to produce different competitive unit activations according to information content obtained in learning. Competition is realized by maximizing mutual information between input patterns and competitive units. Competitive unit outputs are computed by the inverse of distance between input patterns and competitive unit. As distance is smaller, a neuron tends to fire strongly. Thus, winning neurons represent faithfully input patterns. We applied our method to a road classification problem. Experimental results confirmed that the new method could produce more explicit self-organizing maps than conventional self-organizing methods.