FirstOrder Methods for Sparse Covariance Selection
TITLE: FirstOrder Methods for Sparse Covariance Selection.
AUTHORS: Alexandre d'Aspremont, Onureena Banerjee, Laurent El Ghaoui
ABSTRACT: Given a sample covariance matrix, we solve a maximum likelihood problem penalized by the number of nonzero coefficients in the inverse covariance matrix. Our objective is to find a sparse representation of the sample data and to highlight conditional independence relationships between the sample variables. We first formulate a convex relaxation of this combinatorial problem, we then detail two efficient firstorder algorithms with low memory requirements to solve largescale, dense problem instances.
STATUS: SIAM Journal on Matrix Analysis and its Applications, 30(1), pp. 5666, February 2008.
ArXiv PREPRINT: math.OC/0609812
PAPER: FirstOrder Methods for Sparse Covariance Selection in pdf
