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Latent variable graphical model selection via convex optimization. 2010a. Available at http://arxiv.org/abs/1008.1290

By Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky

Abstract

Suppose we observe samples of a subset of a collection of random variables. No additional information is provided about the number of latent variables, nor of the relationship between the latent and observed variables. Is it possible to discover the number of latent components, and to learn a statistical model over the entire collection of variables? We address this question in the setting in which the latent and observed variables are jointly Gaussian, with the conditional statistics of the observed variables conditioned on the latent variables being specified by a graphical model. As a first step we give natural conditions under which such latent-variable Gaussian graphical models are identifiable given marginal statistics of only the observed variables. Essentially these conditions require that the conditional graphical model among the observed variables is sparse, while the effect of the latent variables is “spread out ” over most of the observed variables. Next we propose a tractable convex program based on regularized maximum-likelihood for model selection in this latent-variable setting; the regularizer uses both the ℓ1 norm an

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.7811
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