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Advances in Data Analysis and Classification
Erschienen in: Advances in Data Analysis and Classification 3/2013

01.09.2013 | Regular Article

Dimension reduction for model-based clustering via mixtures of multivariate \(t\)-distributions

verfasst von: Katherine Morris, Paul D. McNicholas, Luca Scrucca

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 3/2013

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Abstract

We introduce a dimension reduction method for model-based clustering obtained from a finite mixture of \(t\)-distributions. This approach is based on existing work on reducing dimensionality in the case of finite Gaussian mixtures. The method relies on identifying a reduced subspace of the data by considering the extent to which group means and group covariances vary. This subspace contains linear combinations of the original data, which are ordered by importance via the associated eigenvalues. Observations can be projected onto the subspace and the resulting set of variables captures most of the clustering structure available in the data. The approach is illustrated using simulated and real data, where it outperforms its Gaussian analogue.
Vorheriger Artikel Model-based clustering of probability density functions
Nächster Artikel Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas

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Metadaten
Titel
Dimension reduction for model-based clustering via mixtures of multivariate -distributions
verfasst von
Katherine Morris
Paul D. McNicholas
Luca Scrucca
Publikationsdatum
01.09.2013
Verlag
Springer Berlin Heidelberg
Erschienen in
Advances in Data Analysis and Classification / Ausgabe 3/2013
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI
https://doi.org/10.1007/s11634-013-0137-3

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