Projection Pursuit Fitting Gaussian Mixture Models

Gaussian mixture models (GMMs) are widely used to model complex distributions. Usually the parameters of the GMMs are determined in a maximum likelihood (ML) framework. A practical deficiency of ML fitting of the GMMs is the poor performance when dealing with high-dimensional data since a large sample size is needed to match the numerical accuracy that is possible in low dimensions. In this paper we propose a method for fitting the GMMs based on the projection pursuit (PP) strategy. By means of simulations we show that the proposed method outperforms ML fitting of the GMMs for small sizes of training sets.