Selective potentiality maximization for input neuron selection in self-organizing maps

The present paper proposes a new type of information-theoretic method to enhance the potentiality of input neurons for improving the class structure of the self-organizing maps (SOM). The SOM has received much attention in neural networks, because it can be used to visualize input patterns, in particular, to clarify class structure. However, it has been observed that the good performance of visualization is limited to relatively simple data sets. To visualize more complex data sets, it is needed to develop a method to extract main characteristics of input patterns more explicitly. For this, several information-theoretic methods have been developed with some problems. One of the main problems is that the method needs much heavy computation to obtain the main features, because the computational procedures to obtain information content should be repeated many times. To simplify the procedures, a new measure called “potentiality” of input neurons is proposed. The potentiality is based on the variance of connection weights for input neurons and it can be computed without the complex computation of information content. The method was applied to the artificial and symmetric data set and the biodegradation data from the machine learning database. Experimental results showed that the method could be used to enhance a smaller number of input neurons. Those neurons were effective in intensifying class boundaries for clearer class structures. The present results show the effectiveness of the new measure of the potentiality for improved visualization and class structure.