A Pathwise Algorithm for Covariance Selection
Optimization and Control
2010-10-12 v2
Abstract
Covariance selection seeks to estimate a covariance matrix by maximum likelihood while restricting the number of nonzero inverse covariance matrix coefficients. A single penalty parameter usually controls the tradeoff between log likelihood and sparsity in the inverse matrix. We describe an efficient algorithm for computing a full regularization path of solutions to this problem.
Cite
@article{arxiv.0908.0143,
title = {A Pathwise Algorithm for Covariance Selection},
author = {Vijay Krishnamurthy and Alexandre d'Aspremont},
journal= {arXiv preprint arXiv:0908.0143},
year = {2010}
}
Comments
More details & numerical experiments. Not all figures could be uploaded on arXiv. Please get local pdf file for complete numerical results