EM algorithms for multivariate normal mixture decomposition have been recently proposed in order to maximize the likelihood function in a constrained parameter space having no singularities and a reduced number of spurious local maxima. However, such approaches require some a priori information about the eigenvalues of the covariance matrices. The behavior of the EM algorithm near a degenerated solution is investigated. The obtained theoretical results would suggest a new kind of constraint based on the dissimilarity between two consecutive updates of the eigenvalues of each covariance matrix. The performances of such a ‘‘dynamic’’ constraint are evaluated on the grounds of some numerical experiments.

Degeneracy of the EM algorithm for the MLE of multivariate Gaussian mixtures and dynamic constraints / Ingrassia, S; Rocci, R. - In: COMPUTATIONAL STATISTICS & DATA ANALYSIS. - ISSN 0167-9473. - 55:(2011), pp. 1715-1725. [10.1016/j.csda.2010.10.026]

Degeneracy of the EM algorithm for the MLE of multivariate Gaussian mixtures and dynamic constraints

ROCCI R
2011

Abstract

EM algorithms for multivariate normal mixture decomposition have been recently proposed in order to maximize the likelihood function in a constrained parameter space having no singularities and a reduced number of spurious local maxima. However, such approaches require some a priori information about the eigenvalues of the covariance matrices. The behavior of the EM algorithm near a degenerated solution is investigated. The obtained theoretical results would suggest a new kind of constraint based on the dissimilarity between two consecutive updates of the eigenvalues of each covariance matrix. The performances of such a ‘‘dynamic’’ constraint are evaluated on the grounds of some numerical experiments.
2011
mixture models; EM algorithm degeneracy; dynamic constraints
01 Pubblicazione su rivista::01a Articolo in rivista
Degeneracy of the EM algorithm for the MLE of multivariate Gaussian mixtures and dynamic constraints / Ingrassia, S; Rocci, R. - In: COMPUTATIONAL STATISTICS & DATA ANALYSIS. - ISSN 0167-9473. - 55:(2011), pp. 1715-1725. [10.1016/j.csda.2010.10.026]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1317592
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