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Advances in Data Analysis and Classification

Erschienen in:

21.11.2019 | Regular Article

Mixtures of skewed matrix variate bilinear factor analyzers

verfasst von: Michael P. B. Gallaugher, Paul D. McNicholas

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 2/2020

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Abstract

In recent years, data have become increasingly higher dimensional and, therefore, an increased need has arisen for dimension reduction techniques for clustering. Although such techniques are firmly established in the literature for multivariate data, there is a relative paucity in the area of matrix variate, or three-way, data. Furthermore, the few methods that are available all assume matrix variate normality, which is not always sensible if cluster skewness or excess kurtosis is present. Mixtures of bilinear factor analyzers using skewed matrix variate distributions are proposed. In all, four such mixture models are presented, based on matrix variate skew-t, generalized hyperbolic, variance-gamma, and normal inverse Gaussian distributions, respectively.

Vorheriger Artikel Gaussian mixture modeling and model-based clustering under measurement inconsistency

Nächster Artikel Data projections by skewness maximization under scale mixtures of skew-normal vectors

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Titel: Mixtures of skewed matrix variate bilinear factor analyzers
verfasst von: Michael P. B. Gallaugher
Paul D. McNicholas
Publikationsdatum: 21.11.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Advances in Data Analysis and Classification / Ausgabe 2/2020
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-019-00377-4

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