Determination of the number of components in Gaussian mixtures using agglomerative clustering

Abstract

Modeling data sets by mixtures is a common technique in many pattern recognition applications. The expectation maximization (EM) algorithm for mixture decomposition suffers from the disadvantage that the number of components in the mixture needs to be specified. In this paper, we propose a new objective function, the minimum of which gives the number of components automatically. The proposed method, known as the agglomerative Gaussian mixture decomposition algorithm, is then used to determine the number of hidden nodes in a radial basis function network. We present results on real data sets which indicate that the proposed method is not sensitive to initialization and gives better classification rates.