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Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions

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dc.contributor.advisor McNicholas, Paul
dc.contributor.author Morris, Katherine
dc.date.accessioned 2012-08-21T13:48:14Z
dc.date.available 2012-08-21T13:48:14Z
dc.date.copyright 2012-07
dc.date.created 2012-07-27
dc.date.issued 2012-08-21
dc.identifier.uri http://hdl.handle.net/10214/3863
dc.description.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 how much 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. en_US
dc.description.sponsorship Paul McNicholas en_US
dc.language.iso en en_US
dc.subject mclust en_US
dc.subject tEIGEN en_US
dc.subject model-based en_US
dc.subject clustering en_US
dc.subject dimension en_US
dc.subject reduction en_US
dc.subject multivariate en_US
dc.subject t-mixtures en_US
dc.title Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions en_US
dc.type Thesis en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Master of Science en_US
dc.degree.department Department of Mathematics and Statistics en_US
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