title: Regularization Paths for Generalized Linear Models via Coordinate Descent.
abstract: We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ℓ(1) (the lasso), ℓ(2) (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.
authors.affiliation: Department of Statistics, Stanford University.
authors.forename: Jerome
authors.initials: J
authors.lastname: Friedman
authors.forename: Trevor
authors.initials: T
authors.lastname: Hastie
authors.forename: Rob
authors.initials: R
authors.lastname: Tibshirani
references.citation: Biostatistics. 2008 Jul;9(3):432-41
references.pmid: 18079126
references.citation: Proc Natl Acad Sci U S A. 2001 Dec 18;98(26):15149-54
references.pmid: 11742071
references.citation: Bioinformatics. 2003 Nov 22;19(17):2246-53
references.pmid: 14630653
references.citation: Biostatistics. 2004 Jul;5(3):427-43
references.pmid: 15208204
references.citation: Science. 1999 Oct 15;286(5439):531-7
references.pmid: 10521349
references.citation: Ann Appl Stat. 2011 Jan 1;5(1):232-253
references.pmid: 22081779
references.citation: Bioinformatics. 2009 Mar 15;25(6):714-21
references.pmid: 19176549
references.citation: Stat Med. 1997 Feb 28;16(4):385-95
references.pmid: 9044528
references.citation: IEEE Trans Pattern Anal Mach Intell. 2005 Jun;27(6):957-68
references.pmid: 15943426
references.citation: Bioinformatics. 2004 Dec 12;20(18):3583-93
references.pmid: 15466910