A clustering algorithm to minimize recognition error functions

Abstract

Pattern recognition methods in which multiple templates are prepared for each category and which recognize input patterns using those templates usually partition training patterns into clusters and generate templates as centroids of each cluster. However, most clustering algorithms proposed in the literature aim at minimizing the square error distortion measure when each cluster is represented by its centroid. Therefore the result obtained is not necessarily optimal from the viewpoint of discrimination. This paper describes a new clustering algorithm which is a method of obtaining an optimum solution to a template generation problem. The simulated annealing is used for searching for a cluster partition that minimizes the “degree” of recognition error when all training patterns are recognized by using centroids as templates. This paper also describes a vowel template generation problem in speech recognition as an example of pattern discrimination problems. The new algorithm yielded better experimental results than the LBG algorithm and the LVQ algorithm.