First-order methods for sparse covariance selection
最优化与控制
2007-06-13 v1 统计理论
统计理论
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
@article{arxiv.math/0609812,
title = {First-order methods for sparse covariance selection},
author = {Alexandre d'Aspremont and Onureena Banerjee and Laurent El Ghaoui},
journal= {arXiv preprint arXiv:math/0609812},
year = {2007}
}