Regularization and Variable Selection Via the Elastic Net

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ID: 289112
2005
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Ranked #69 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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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 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.
Reference Key
openalex_W2122825543 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hui Zou, Trevor Hastie
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 2005
DOI
10.1111/j.1467-9868.2005.00503.x
URL
https://academic.oup.com/jrsssb/article-pdf/67/2/301/49795094/jrsssb_67_2_301.pdf
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