Statistical classification using a linear mixture of two multinormal probability densities

Abstract

The paper describes an estimation-maximization algorithm to estimate the parameters of a probability density model consisting of a linear mixture of two multinormal distributions. Superior classification results to those obtained using the multinormal distribution or the k-nearest neighbour rule were obtained with this model on two difficult data sets.