The Latent Bernoulli-Gauss Model for Data Analysis
Machine Learning
2010-07-06 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1007.0660,
title = {The Latent Bernoulli-Gauss Model for Data Analysis},
author = {Amnon Shashua and Gabi Pragier},
journal= {arXiv preprint arXiv:1007.0660},
year = {2010}
}