2008 SparseInvarianceCovarianceEstimation

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Subject Headings: Sparse Learning.



We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm — the graphical lasso — that is remarkably fast: It solves a 1000-node problem (~500000 parameters) in at most a minute and is 30–4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.,

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2008 SparseInvarianceCovarianceEstimationJerome H. Friedman
Trevor Hastie
Robert Tibshirani
Sparse Inverse Covariance Estimation with the Graphical LassoBiostatisticshttp://arxiv.org/pdf/0708.351710.1093/biostatistics/kxm0452008
AuthorJerome H. Friedman +, Trevor Hastie + and Robert Tibshirani +
doi10.1093/biostatistics/kxm045 +
journalBiostatistics +
titleSparse Inverse Covariance Estimation with the Graphical Lasso +
titleUrlhttp://arxiv.org/pdf/0708.3517 +
year2008 +