Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Regularization Paths for Generalized Linear Models via Coordinate Descent | Friedman | Journal of Statistical Software
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.

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Paper: Regularization Paths for Generalized Linear Models via Coordinate Descent     Download PDF (Downloads: 67084)
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DOI: 10.18637/jss.v033.i01

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