Self-Organizing Selective Potentiality Learning to Detect Important Input Neurons

The present paper proposes a new type of method based on the self-organizing maps to enhance the potentiality of input neurons which can be applied to the extraction important features and improved generalization performance. The importance of input neurons plays important roles in the self-organizing maps. However, little attempts have been made to determine the importance of input neurons, because it has been difficult to measure the importance of neurons in unsupervised learning such as the SOM. Though some information-theoretic methods have been developed to estimate the importance, they need heavy computation to reach the final state. In this context, a new and very simple method is proposed to estimate the importance of input neurons and its performance is experimentally evaluated. The new method is based on the concept of "potentiality". The potentiality means the variance of input neurons toward competitive neurons. When the potentiality becomes higher, the variance of input neurons becomes larger. The self-organizing maps with this potentiality was applied to the two well-known data sets. In both cases, the smaller number of important neurons could be extracted and generalization performance in the supervised mode was much improved compared with that by the conventional methods. However, the map quality in terms of quantization and topographic errors may be degraded. This implies that in actual application, it is needed to compromise between improved generalization and the quality of maps. Though some problems should be solved, the present method of potentiality is simple and strong enough for extracting the importance of input neurons.