Sparse inverse covariance estimation with the lasso

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dc.creatorFriedman, Jerome
dc.creatorHastie, Trevor
dc.creatorTibshirani, Robert
dc.date2007-08-27
dc.date.accessioned2026-07-07T08:25:48Z
dc.date.available2026-07-07T08:25:48Z
dc.descriptionWe 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.
dc.descriptionsubmitted
dc.identifierhttps://arxiv.org/abs/0708.3517
dc.identifierhttp://arxiv.org/abs/0708.3517
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/136741
dc.subjectMethodology
dc.subjectC5C60
dc.titleSparse inverse covariance estimation with the lasso
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