2008 SparseInvarianceCovarianceEstimation
- (Friedman et al., 2008) ⇒ Jerome H. Friedman, Trevor Hastie, Robert Tibshirani. (2008). “Sparse Inverse Covariance Estimation with the Graphical Lasso.” In: Biostatistics, 9(3). doi:10.1093/biostatistics/kxm045.
Subject Headings: Sparse Learning.
Quotes
- Keywords: Gaussian Covariance, Graphical Model, L1, Lasso.
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.,
| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2008 SparseInvarianceCovarianceEstimation | Jerome H. Friedman Trevor Hastie | Sparse Inverse Covariance Estimation with the Graphical Lasso | http://arxiv.org/pdf/0708.3517 | 10.1093/biostatistics/kxm045 |