In this paper, we propose a new method to extract explicit features for competitive learning as well as self-organizing maps. The method aims to enhance final internal representations by conventional methods. We first train networks by conventional methods and compute enhanced information by focusing upon some specific input units or variables. Because we focus upon some specific inputs and activate competitive units, this enhancement is called partial enhancement. Then, networks are retrained to imitate the states obtained by partial enhancement. Final representations obtained by this retraining generate representations influenced by these specific variables. We applied the method to the famous Iris problem and the air pollution problem. In both problems, partial enhancement methods could produce clearer feature maps, superior to those obtained by self-organizing maps.
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