A novel vector quantizer for pattern classification tasks

We present a novel vector quantization method for pattern classification tasks. The input space is quantized into volume regions by code-vectors formed by weights of neurons. During training, the volume regions are merged and split, depending upon the ambiguity in classification, measured using Kullback-Leibler divergence. The heuristic followed is to split ambiguous regions, and merge two volume regions if they contain predominant populations of the same class. The neural network forms a generalized Delaunay graph, whose topology changes dynamically with the merging and splitting. The simulation results indicate the utility of the proposed method.