Sparse inverse covariance estimation with the graphical lasso.


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 ( approximately 500,000 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.

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@article{Friedman2008SparseIC, title={Sparse inverse covariance estimation with the graphical lasso.}, author={Jerome H. Friedman and Trevor J. Hastie and Robert Tibshirani}, journal={Biostatistics}, year={2008}, volume={9 3}, pages={432-41} }