Variational learning for Gaussian mixture models.
IEEE Trans Syst Man Cybern B Cybern
; 36(4): 849-62, 2006 Aug.
Article
em En
| MEDLINE
| ID: mdl-16903369
ABSTRACT
This paper proposes a joint maximum likelihood and Bayesian methodology for estimating Gaussian mixture models. In Bayesian inference, the distributions of parameters are modeled, characterized by hyperparameters. In the case of Gaussian mixtures, the distributions of parameters are considered as Gaussian for the mean, Wishart for the covariance, and Dirichlet for the mixing probability. The learning task consists of estimating the hyperparameters characterizing these distributions. The integration in the parameter space is decoupled using an unsupervised variational methodology entitled variational expectation-maximization (VEM). This paper introduces a hyperparameter initialization procedure for the training algorithm. In the first stage, distributions of parameters resulting from successive runs of the expectation-maximization algorithm are formed. Afterward, maximum-likelihood estimators are applied to find appropriate initial values for the hyperparameters. The proposed initialization provides faster convergence, more accurate hyperparameter estimates, and better generalization for the VEM training algorithm. The proposed methodology is applied in blind signal detection and in color image segmentation.
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Reconhecimento Automatizado de Padrão
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Inteligência Artificial
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Interpretação de Imagem Assistida por Computador
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Modelos Estatísticos
Idioma:
En
Ano de publicação:
2006
Tipo de documento:
Article