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 ℓ<sub>1</sub> (the lasso), ℓ<sub>2</sub> (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.
Keywords
Coordinate descentElastic net regularizationLasso (programming language)Regularization (linguistics)Linear regressionRegressionComputer scienceGeneralized linear modelLinear modelRegular polygonLogistic regressionMathematicsRidgeAlgorithmMathematical optimizationApplied mathematicsArtificial intelligenceStatistics
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Publication Info
- Year
- 2010
- Type
- article
- Volume
- 33
- Issue
- 1
- Citations
- 15877
- Access
- Closed
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Cite This
Jerome H. Friedman,
Trevor Hastie,
Robert Tibshirani
(2010).
Regularization Paths for Generalized Linear Models via Coordinate Descent.
Journal of Statistical Software
, 33
(1)
.
https://doi.org/10.18637/jss.v033.i01
Identifiers
- DOI
- 10.18637/jss.v033.i01