Bayesian feature and model selection for Gaussian mixture models.

Constantinos Constantinopoulos; Michalis K Titsias; Aristidis Likas

doi:10.1109/tpami.2006.111

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Bayesian feature and model selection for Gaussian mixture models.

Constantinos Constantinopoulos·Michalis K Titsias·Aristidis Likas

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Abstract

We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of a mixture model formulation that takes into account the saliency of the features and a Bayesian approach to mixture learning that can be used to estimate the number of mixture components. The proposed learning algorithm follows the variational framework and can simultaneously optimize over the number of components, the saliency of the features, and the parameters of the mixture model. Experimental results using high-dimensional artificial and real data illustrate the effectiveness of the method.

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Algorithms Artificial Intelligence Computer Simulation Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Models, Statistical Normal Distribution Pattern Recognition, Automated Evaluation Study Journal Article Research Support, Non-U.S. Gov't

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Constantinos Constantinopoulos, Michalis K Titsias, Aristidis Likas. 2006. Bayesian feature and model selection for Gaussian mixture models.. https://doi.org/10.1109/tpami.2006.111

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