GRADUAL INFORMATION MAXIMIZATION IN INFORMATION ENHANCEMENT TO EXTRACT IMPORTANT INPUT NEURONS

摘要

In this paper, we propose a new type of information-theoretic method called "gradual information maximization" to detect important input neurons (variables) in the self-organizing maps. The information enhancement method has been developed to detect important components in neural networks. However, in the information enhancement method, we have found that information for detecting important neurons is not necessarily acquired. The gradual information maximization aims to acquire information generated in the course of learning as much as possible. This means that information accumulated in every stage of learning can be used for the detection of important neurons. We applied the method to the analysis of a public poll opinion toward a city government in Tokyo metropolitan area. The method extracted clearly one important variable of "meeting places." By examining carefully the public documents of the city, we found that the problem of "meeting places" in the city was considered to be one of the most serious financial problems. Thus, the finding by the gradual information maximization represents an important problem in the city.