Some practical aspects of exploratory projection pursuit
SIAM Journal on Scientific Computing
Linear Discriminant Analysis for Two Classes via Removal of Classification Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning in graphical models
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Nonparametric discriminant analysis via recursive optimization ofPatrick-Fisher distance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Recursive training of neural networks for classification
IEEE Transactions on Neural Networks
Bayesian classifiers based on kernel density estimation: Flexible classifiers
International Journal of Approximate Reasoning
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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.