Entropy-driven structural adaptation in sample-space self-organizing feature maps for pattern classification

The relationship of the self-organizing map to the general problem of non-parametric density estimation has brought about diverse applications of this network to vector quantization and pattern recognition problems. However, the requirement of deciding a priori the number of processing units to use limits the ability of the network to deliver satisfactory solutions. In this paper we consider a new structural adaptation approach that is based on the measurement and monitoring of the relative entropy during the learning phase of self-organizing feature maps with sample-space neighborhoods and trained in a batch mode. Results on the classification accuracy of the networks built with the proposed scheme are presented, together with some application examples