Feature Discovery by Information Loss

Abstract

In this paper, we propose a new approach called information loss to feature detection in competitive learning. The information loss is defined by the difference between a full network and a network without some elements. If this deletion significantly decreases the amount of information contained in a network, the elements are considered to be important and are expected to play a very important role. The method was applied to artificial and symmetric data to show the features extracted by the information loss. Then, we applied the method to the classification of OECD countries. The experimental results confirmed that the method was efficient enough to detect main features comparable to those detected by the conventional SOM.