Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions

Authors:
Jeffrey L. Andrews;Paul D. Mcnicholas
Affiliations:
Department of Mathematics and Statistics, University of Guelph, Guelph, Canada N1G 2W1;Department of Mathematics and Statistics, University of Guelph, Guelph, Canada N1G 2W1
Venue:
Statistics and Computing
Year:
2012

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Abstract

The last decade has seen an explosion of work on the use of mixture models for clustering. The use of the Gaussian mixture model has been common practice, with constraints sometimes imposed upon the component covariance matrices to give families of mixture models. Similar approaches have also been applied, albeit with less fecundity, to classification and discriminant analysis. In this paper, we begin with an introduction to model-based clustering and a succinct account of the state-of-the-art. We then put forth a novel family of mixture models wherein each component is modeled using a multivariate t-distribution with an eigen-decomposed covariance structure. This family, which is largely a t-analogue of the well-known MCLUST family, is known as the tEIGEN family. The efficacy of this family for clustering, classification, and discriminant analysis is illustrated with both real and simulated data. The performance of this family is compared to its Gaussian counterpart on three real data sets.