Information loss to extract distinctive features in competitive learning

In this paper, we propose a new type of information- theoretic method called information loss to evaluate the importance of input variables. Information loss is defined by difference between information content with all input variables and without an input variable. Thus, information loss represents to what extent an input variable plays an important role in learning. By experiments, we can see that information loss extracts distinctive features by which different input patterns are separated. We applied the information loss to a semantic differential problem, that is, an image of information science education. We successfully extracted the main features of input patterns and succeeded in revealing an image of information science education.