Regularization Paths for Generalized Linear Models via Coordinate Descent

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Authors Jerome H. Friedman, Trevor J. Hastie, Rob Tibshirani
Journal/Conference Name Journal of statistical software
Paper Category
Paper 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.
Date of publication 2010
Code Programming Language R
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