Sparse inverse covariance estimation with the graphical lasso
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ID: 289383
2007
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Abstract
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.
| Reference Key |
openalex_W2132555912
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|---|---|
| Authors | Jerome H. Friedman, Trevor Hastie, Robert Tibshirani |
| Journal | epidemiology biostatistics and public health |
| Year | 2007 |
| DOI |
10.1093/biostatistics/kxm045
|
| URL | |
| Keywords | Keywords not found |
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