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Title of Journal: Stat Comput

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Abbravation: Statistics and Computing

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Springer US

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DOI

10.1007/bf00407211

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1573-1375

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Parsimonious Gaussian mixture models

Authors: Paul David McNicholas Thomas Brendan Murphy
Publish Date: 2008/04/19
Volume: 18, Issue: 3, Pages: 285-296
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Abstract

Parsimonious Gaussian mixture models are developed using a latent Gaussian model which is closely related to the factor analysis model These models provide a unified modeling framework which includes the mixtures of probabilistic principal component analyzers and mixtures of factor of analyzers models as special casesIn particular a class of eight parsimonious Gaussian mixture models which are based on the mixtures of factor analyzers model are introduced and the maximum likelihood estimates for the parameters in these models are found using an AECM algorithm The class of models includes parsimonious models that have not previously been developed

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