Regularization paths for generalized linear models via coordinate descent

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Regularization paths for generalized linear models via coordinate descent (2009)

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by Jerome Friedman , Trevor Hastie , Rob Tibshirani

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723 - 15 self

BibTeX

@MISC{Friedman09regularizationpaths,
    author = {Jerome Friedman and Trevor Hastie and Rob Tibshirani},
    title = {Regularization paths for generalized linear models via coordinate descent },
    year = {2009}
}

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Abstract

We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, twoclass logistic regression, and multinomial 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.

Keyphrases

generalized linear model regularization path coordinate descent large problem fast algorithm twoclass logistic regression algorithm use cyclical coordinate descent elastic net new algorithm sparse feature comparative timing convex penalty multinomial regression problem linear regression

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