Collective Information Maximization for Self-Organizing Maps

The present paper shows that a self-organizing process can be realized by maximizing information between input patterns and competitive units. We have already shown that information maximization corresponds to competitive processes. Thus, if cooperation processes can be incorporated in information maximization, self-organizing maps can naturally be realized by information maximization. By using the weighted sum of distances among neurons or collected distance, we successfully incorporate cooperation processes in the main mechanism of information maximization. For comparing our method with the standard SOM, we applied the method to the well-known artificial data and show that clear feature maps can be obtained by maximizing information