Regularization and variable selection via the Elastic Net

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Regularization and variable selection via the Elastic Net (2004)

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by Hui Zou , Trevor Hastie

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969 - 12 self

BibTeX

@MISC{Zou04regularizationand,
    author = {Hui Zou and Trevor Hastie},
    title = {Regularization and variable selection via the Elastic Net },
    year = {2004}
}

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Abstract

We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in (out) the model together. The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the p n case. An efficient algorithm called LARS-EN is proposed for computing elastic net regularization paths efficiently, much like the LARS algorithm does for the lasso.

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