Sparse inverse covariance estimation with the graphical lasso
Top Cited Papers
Open Access
- 12 December 2007
- journal article
- research article
- Published by Oxford University Press (OUP) in Biostatistics
- Vol. 9 (3) , 432-441
- https://doi.org/10.1093/biostatistics/kxm045
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.Keywords
All Related Versions
This publication has 5 references indexed in Scilit:
- Model selection and estimation in the Gaussian graphical modelBiometrika, 2007
- High-dimensional graphs and variable selection with the LassoThe Annals of Statistics, 2006
- Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell DataScience, 2005
- Convex OptimizationPublished by Cambridge University Press (CUP) ,2004
- Determinant Maximization with Linear Matrix Inequality ConstraintsSIAM Journal on Matrix Analysis and Applications, 1998