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A non asymptotic penalized criterion for Gaussian mixture model selection

Cathy Maugis 1 Bertrand Michel 1
1 SELECT - Model selection in statistical learning
LMO - Laboratoire de Mathématiques d'Orsay, Inria Saclay - Ile de France
Abstract : Specific Gaussian mixtures are considered to solve simultaneously variable selection and clustering problems. A non asymptotic penalized criterion is proposed to choose the number of mixture components and the relevant variable subset. Because of the non linearity of the associated Kullback-Leibler contrast on Gaussian mixtures, a general model selection theorem for MLE proposed by Massart (2007) is used to obtain the penalty function form. This theorem requires to control the bracketing entropy of Gaussian mixture families. The ordered and non-ordered variable selection cases are both addressed in this paper.
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Submitted on : Wednesday, June 4, 2008 - 12:57:47 PM
Last modification on : Friday, January 21, 2022 - 4:04:17 AM
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  • HAL Id : inria-00284613, version 2



Cathy Maugis, Bertrand Michel. A non asymptotic penalized criterion for Gaussian mixture model selection. [Research Report] RR-6549, INRIA. 2008. ⟨inria-00284613v2⟩



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