First-Order Methods for Sparse Covariance Selection

  • Authors: A. d'Aspremont, O. Banerjee, and L. El Ghaoui.

  • Status: SIAM. J. Matrix Anal. & Appl. 30, 56 (2008).

  • 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 first-order algorithms with low memory requirements to solve large-scale, dense problem instances.

  • Bibtex reference:

	Author = {A. {d'Aspremont} and O. Banerjee and L. {El Ghaoui}},
	Journal = {SIAM. J. Matrix Anal. \& Appl.},
	Number = {56},
	Title = {First-Order Methods for Sparse Covariance Selection},
	Volume = {30},
	Year = {2008}}