PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Factorial mixture of Gaussians and the marginal independence model
Ricardo Silva and Zoubin Ghahramani
JMLR Workshop and Conference Proceedings: AISTATS 2009 Volume 5, pp. 520-527, 2009.

Abstract

Marginal independence constraints play an important role in learning with graphical models. One way of parameterizing a model of marginal independencies is by building a latent variable model where two independent observed variables have no common latent source. In sparse domains, however, it might be advantageous to model the marginal ob- served distribution directly, without explic- itly including latent variables in the model. There have been recent advances in Gaussian and binary models of marginal independence, but no models with non-linear dependencies between continuous variables has been pro- posed so far. In this paper, we describe how to generalize the Gaussian model of marginal independencies based on mixtures, and how to learn parameters. This requires a non- standard parameterization and raises difficult non-linear optimization issues.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5607
Deposited By:Ricardo Silva
Deposited On:08 March 2010