Mixtures of Common Skew-t Factor Analyzers

Abstract

A mixture of common skew-t factor analyzers model is introduced for model-based clustering of high-dimensional data. By assuming common component factor loadings, this model allows clustering to be performed in the presence of a large number of mixture components or when the number of dimensions is too large to be well-modelled by the mixtures of factor analyzers model or a variant thereof. Furthermore, assuming that the component densities follow a skew-t distribution allows robust clustering of skewed data. The alternating expectation-conditional maximization algorithm is employed for parameter estimation. We demonstrate excellent clustering performance when our model is applied to real and simulated data.This paper marks the first time that skewed common factors have been used.

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• A Mixture of Generalized Hyperbolic Distributions
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• Dimension reduction for model-based clustering
• Mixtures of Variance-Gamma Distributions
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• Finite Mixtures of Skewed Matrix Variate Distributions
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• A Mixture of SDB Skew-t Factor Analyzers
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• Flexible Clustering for High-Dimensional Data via Mixtures of Joint Generalized Hyperbolic Models