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

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. / Paul McNicholas

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OGU.10214/3863
Date21 August 2012
CreatorsMorris, Katherine
ContributorsMcNicholas, Paul
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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