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.
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