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arXiv · 0708.3517

Sparse inverse covariance estimation with the lasso

Jerome Friedman·Trevor Hastie·Robert Tibshirani
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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 that is remarkably fast: in the worst cases, it solves a 1000 node problem (~500,000 parameters) in about a minute, and is 50 to 2000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinhausen and Buhlmann (2006). We illustrate the method on some cell-signaling data from proteomics.

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Jerome Friedman, Trevor Hastie, Robert Tibshirani. 2007-08-27. Sparse inverse covariance estimation with the lasso. https://arxiv.org/abs/0708.3517

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