A Pathwise Algorithm for Covariance Selection
TITLE: A Pathwise Algorithm for Covariance Selection.
AUTHORS: Vijay Krishnamurthy, Selin Damla Ahipasaoglu, Alexandre d'Aspremont
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.
STATUS: Optimization for Machine Learning, S. Sra, S. Nowozin and S. Wright, editors, MIT Press, 2011.
ArXiv PREPRINT: 0908.0143
PAPER: A Pathwise Algorithm for Covariance Selection in pdf.
CODE: The COVPATH library is available on CRAN
