Forced Information Maximization to Accelerate Information-Theoretic Competitive Learning

Information-theoretic competitive learning has been proved to be more general and more flexible type of competitive learning. However, one of the major shortcomings of this method is that it is sometimes very slow in learning. To overcome this problem, we introduce forced information used to force networks to increase information by supposing maximum information. We applied the method to an artificial data as well as a student survey. In both cases, we observed that information was very rapidly increased to stable points. Compared with results by the principal component analysis, our method showed more clearly the main features of input patterns. In addition, we can more easily explain a main mechanism of feature detection by forced information.