R. Kamimura, O. Uchida, and S. Hashimoto (Japan)
: mutual information maximization, competi tive learning, winner-take-all, Minkowski distance
In this paper, we propose a new network-growing method to accelerate learning and to extract explicit features in complex input patterns. We have so far proposed a new type of network-growing algorithm called greedy network growing algorithm[1],[2]. Though the method have shown some potentiality to extract salient features, we have ob served that the method is slow in learning, and sometimes it cannot produce a state where information is large enough to produce explicit internal representations. To remedy this shortcoming, we introduce here Minkowski distance between input patterns and connection weights used to produce competitive unit outputs. When the parameter for Minkowski distance is larger, some detailed parts in input patterns can be eliminated, which enables networks to converge faster and to extract main parts of input patterns. We applied our new method to an economic data analysis. Experimental results confirm that a new method with Minkowski distance can significantly accelerate learning, and clearer features can be extracted.
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