Abstract
In this paper, we propose structural enhanced information for detecting main features in input patterns. In structural enhanced information, three types of enhanced information can be differentiated, that is, the first-, the second- and the third-order enhanced information. The first-order information is related to the enhancement of competitive units themselves through some elements in a network, and the second-order information is dependent upon the enhancement of competitive units with input patterns. Then, the third-order information is obtained by subtracting the effect of the first-order information from the second-order information. Thus, the third-order information more explicitly represents information on input patterns. With this structural enhanced information, we can estimate more detailed features in input patterns. We applied the method to the well-known Iris problem. In both problems, we succeeded in extracting detailed and important features especially by using the third-order information.KeywordsMutual InformationInput PatternFeature DetectionAverage Mutual InformationInput UnitThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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