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- Title
Sparse inverse covariance estimation with the graphical lasso.
- Authors
Jerome Friedman; Trevor Hastie; Robert Tibshirani
- Abstract
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.
- Subjects
ANALYSIS of covariance; ALGORITHMS; PROTEOMICS; MOLECULAR biology
- Publication
Biostatistics, 2008, Vol 9, Issue 3, p432
- ISSN
1465-4644
- Publication type
Article
- DOI
10.1093/biostatistics/kxm045