Similarity interaction in information-theoretic self-organizing maps
Ryotaro Kamimura · International Journal of General Systems · 2012
In this paper, we propose a new information-theoretic computational method called ‘similarity interaction’ for improving visualization. Due to the fixed arrangement of neurons in the self-organizing maps, similarity between neurons is not necessarily a faithful representation of the actual similarity between neurons. To relax the fixed arrangement, we introduce a method called ‘similarity interaction’, because we integrate the information of connection weights into that of neurons. We applied our method to three problems, namely teaching assistant evaluation, automobile data, and dermatology data. In all three problems, we succeeded in demonstrating the better performance of our method through visual inspection and quantitative evaluation. Our method is the first step towards the interaction of multiple components in a neural network for finer representations of input patterns.