  We present a new latent-variable model employing a Gaussian mixture
integrated with a feature selection procedure (the Bernoulli part of the model)
which together form a "Latent Bernoulli-Gauss" distribution. The model is
applied to MAP estimation, clustering, feature selection and collaborative
filtering and fares favorably with the state-of-the-art latent-variable models.
