First-order methods for sparse covariance selection

被引：196

作者：

D'Aspremont, Alexandre ^{[1
]}

Banerjee, Onureena ^{[2
]}

El Ghaoui, Laurent ^{[2
]}

机构：

[1] Princeton Univ, ORFE Dept, Princeton, NJ 08544 USA

[2] Univ Calif Berkeley, Dept EECS, Berkeley, CA 94720 USA

来源：

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS | 2008年 / 30卷 / 01期

基金：

美国国家科学基金会;

关键词：

covariance selection; semidefinite programming; coordinate descent;

D O I：

10.1137/060670985

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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.

引用

页码：56 / 66

页数：11

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