Regularization and variable selection via the Elastic Net

Regularization and variable selection via the Elastic Net (2005)

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by Hui Zou , Trevor Hastie

Venue:	Journal of the Royal Statistical Society, Series B
Citations:	963 - 12 self

BibTeX

@ARTICLE{Zou05regularizationand,
    author = {Hui Zou and Trevor Hastie},
    title = {Regularization and variable selection via the Elastic Net},
    journal = {Journal of the Royal Statistical Society, Series B},
    year = {2005},
    volume = {67},
    pages = {301--320}
}

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Abstract

Summary. 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 or out of 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 algorithm called LARS-EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lasso.

Keyphrases

elastic net variable selection real world data algorithm lars new regularization grouping effect elastic net regularization path variable selection method satisfactory variable selection method simulation study show similar sparsity