Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Jerome H. Friedman, Trevor Hastie, Rob Tibshirani
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 multi- nomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (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.

Page views:: 95712. Submitted: 2009-04-22. Published: 2010-02-02.
Paper: Regularization Paths for Generalized Linear Models via Coordinate Descent     Download PDF (Downloads: 84005)
Supplements:
glmnet_1.1-4.tar.gz: R source package Download (Downloads: 4838; 63KB)
v33i01.R: R example code to replicate the figures from the paper Download (Downloads: 3705; 1KB)
v33i01-timings.zip: ZIP archive with source code for replicating the timings simulations Download (Downloads: 2138; 13KB)
InternetAd.RData: Data set in R binary format Download (Downloads: 2147; 48KB)
Leukemia.RData: Data set in R binary format Download (Downloads: 2668; 1MB)
NewsGroup.RData: Data set in R binary format Download (Downloads: 2065; 9MB)
Ramaswamy.RData: Data set in R binary format Download (Downloads: 2119; 21MB)
DOI: 10.18637/jss.v033.i01

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.