MDL-based selection of the number of components in mixture models for pattern classification

Abstract

A new method is proposed for selection of the optimal number of components of a mixture model for pattern classification. We approximate a class-conditional density by a mixture of Gaussian components. We estimate the parameters of the mixture components by the EM (Expectation Maximization) algorithm and select the optimal number of components on the basis of the MDL (Minimum Description Length) principle. We evaluate the goodness of an estimated model in a tradeoff between the number of the misclassified training samples and the complexity of the model.