Variational Bayesian feature selection for Gaussian mixture models

In this paper we show that feature selection problem can be formulated as a model selection problem. A Bayesian framework for feature selection in unsupervised learning based on Gaussian mixture models is applied to speech recognition. In the original formulation (Figueiredo (2002)) a minimum message length criterion is used for model selection; we propose a new model selection technique based on variational Bayesian learning that shows a higher robustness to the amount of training data. Results on speech data from the TIMIT database show a high efficiency in determining feature saliency.