Inverse covariance estimation from data with missing values using the Concave-Convex Procedure

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 01 Dec 2014Publisher:IEEEJournal:53rd IEEE Conference on Decision and Control

Authors: Thai, Jerome; Hunter, Timothy; Akametalu, Anayo K.; Tomlin, Claire J.; Bayen, Alexandre M.;

doi: 10.1109/cdc.2014.7040287

Inverse covariance estimation from data with missing values using the Concave-Convex Procedure

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Abstract

We study the problem of estimating sparse precision matrices from data with missing values. We show that the corresponding maximum likelihood problem is a Difference of Convex (DC) program by proving some new concavity results on the Schur complements. We propose a new algorithm to solve this problem based on the ConCave-Convex Procedure (CCCP), and we show that the standard EM procedure is a weaker CCCP for this problem. Numerical experiments show that our new algorithm, called m-CCCP, converges much faster than EM on both synthetic and biology datasets.

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University of California, Berkeley
United States

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