First-order methods for sparse covariance selection

Alexandre d'Aspremont; Laurent El Ghaoui; Onureena Banerjee

REVIEW

First-order methods for sparse covariance selection

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv math/0609812 v1 pith:AIBBMVZJ submitted 2006-09-28 math.OC math.STstat.TH

First-order methods for sparse covariance selection

Alexandre d'Aspremont , Onureena Banerjee , Laurent El Ghaoui This is my paper

classification math.OC math.STstat.TH

keywords covarianceproblemsamplefirst-ordermatrixsolvesparsealgorithms

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

Share X Bluesky LinkedIn Reddit HN

read the original abstract

Given a sample covariance matrix, we solve a maximum likelihood problem penalized by the number of nonzero coefficients in the inverse covariance matrix. Our objective is to find a sparse representation of the sample data and to highlight conditional independence relationships between the sample variables. We first formulate a convex relaxation of this combinatorial problem, we then detail two efficient first-order algorithms with low memory requirements to solve large-scale, dense problem instances.

First-order methods for sparse covariance selection

discussion (0)