Hidden information maximization for feature detection and rule discovery

Abstract

In this paper, We propose a method to maximize the hidden information stored in hidden units. The hidden information is defined by the decrease in uncertainty of hidden units with respect to input patterns. By maximizing the hidden information, the hidden unit can detect features and extract rules behind input patterns. Our method was applied to two problems: an autoencoder to produce six alphabet letters and the assimilation for the formation of plurals and nasalization in an artificial language. In the first problem, the results explicitly confirmed that the features of input patterns could be detected by maximizing the hidden information. In the second experiment, we could clearly see that the rules of the assimilation were extracted by maximizing the hidden information, even if the rules are obscured by some other factors.