Student Survey by Information-Theoretic Competitive Learning

In this paper, we apply our information-theoretic method to a student survey. The information-theoretic method aims to extract main features in input patterns by condensing information contained in input patterns as much as possible. By using 2500 students´ responses to the questionnaire, we could extract the main subjects in which the majority of students have interest. Especially, we could classify students into several groups by their interest. On the other hand, the conventional PCA could not demonstrate specific features in input patters. Thus, the information-theoretic neural methods can open up a new perspective for data analyses.