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2016 | OriginalPaper | Buchkapitel

Unsupervised Component-Wise EM Learning for Finite Mixtures of Skew t-distributions

verfasst von : Sharon X. Lee, Geoffrey J. McLachlan

Erschienen in: Advanced Data Mining and Applications

Verlag: Springer International Publishing

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Abstract

In recent years, finite mixtures of skew distributions are gaining popularity as a flexible tool for modelling data with asymmetric distributional features. Parameter estimation for these mixture models via the traditional EM algorithm requires the number of components to be specified a priori. In this paper, we consider unsupervised learning of skew mixture models where the optimal number of components is estimated during the parameter estimation process. We adopt a component-wise EM algorithm and use the minimum message length (MML) criterion. For illustrative purposes, we focus on the case of a finite mixture of multivariate skew t distributions. The performance of the approach is demonstrated on a real dataset from flow cytometry, where our mixture model was used to provide an automated segmentation of cell populations.

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Titel: Unsupervised Component-Wise EM Learning for Finite Mixtures of Skew t-distributions
verfasst von: Sharon X. Lee
Geoffrey J. McLachlan
Verlag: Springer International Publishing
Buch: Advanced Data Mining and Applications
Print ISBN: 978-3-319-49585-9

Electronic ISBN: 978-3-319-49586-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-49586-6_49

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