Sparse inverse covariance estimation with the lasso

2007/08/27 by Jerome Friedman, Trevor Hastie, Friedman, Jerome +3 · 1 citation
Mathematics · #C5C60 #FOS: Computer and information sciences #Methodology (stat.ME) #msc:C5C60 #stat.ME

paper · pdf · doi:10.48550/arxiv.0708.3517

submitted

arxiv created 2007/08/27 · arxiv updated 2009/12/01

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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