Sparse Covariance Selection via Robust Maximum Likelihood Estimation

Onureena Banerjeee (onureenaeecs.berkeley.edu)
Alexandre d'Aspremont (aspremonprinceton.edu)
Laurent El Ghaoui (elghaouieecs.berkeley.edu)

Abstract: We address a problem of covariance selection, where we seek a trade-off between a high likelihood against the number of non-zero elements in the inverse covariance matrix. We solve a maximum likelihood problem with a penalty term given by the sum of absolute values of the elements of the inverse covariance matrix, and allow for imposing bounds on the condition number of the solution. The problem is directly amenable to now standard interior-point algorithms for convex optimization, but remains challenging due to its size. We first give some results on the theoretical computational complexity of the problem, by showing that a recent methodology for non-smooth convex optimization due to Nesterov can be applied to this problem, to greatly improve on the complexity estimate given by interior-point algorithms. We then examine two practical algorithms aimed at solving large-scale, noisy (hence dense) instances: one is based on a block-coordinate descent approach, where columns and rows are updated sequentially, another applies a dual version of Nesterov's method.

Keywords: Covariance selection, semidefinite programming, first order methods

Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Category 3: Applications -- Science and Engineering (Statistics )

Citation: ArXiV cs.CE/0506023

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Entry Submitted: 07/14/2005
Entry Accepted: 07/14/2005
Entry Last Modified: 07/14/2005

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