Neural Comput - Regularized parameter estimation in high-dimensional gaussian mixture models.

Tópicos

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Resumo

Finite gaussian mixture models are widely used in statistics thanks to their great flexibility. However, parameter estimation for gaussian mixture models with high dimensionality can be challenging because of the large number of parameters that need to be estimated. In this letter, we propose a penalized likelihood estimator to address this difficulty. The [Formula: see text]-type penalty we impose on the inverse covariance matrices encourages sparsity on its entries and therefore helps to reduce the effective dimensionality of the problem. We show that the proposed estimate can be efficiently computed using an expectation-maximization algorithm. To illustrate the practical merits of the proposed method, we consider its applications in model-based clustering and mixture discriminant analysis. Numerical experiments with both simulated and real data show that the new method is a valuable tool for high-dimensional data analysis.

Resumo Limpo

finit gaussian mixtur model wide use statist thank great flexibl howev paramet estim gaussian mixtur model high dimension can challeng larg number paramet need estim letter propos penal likelihood estim address difficulti formula see texttyp penalti impos invers covari matric encourag sparsiti entri therefor help reduc effect dimension problem show propos estim can effici comput use expectationmaxim algorithm illustr practic merit propos method consid applic modelbas cluster mixtur discrimin analysi numer experi simul real data show new method valuabl tool highdimension data analysi

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