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