Regularization and Variable Selection Via the Elastic Net
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2005 · 21,798 citationsOpen access
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.
Cite this paper
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B (Statistical Methodology), 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x
Read it with every claim anchored
Add this paper to a project, ask questions of it, and get answers that point to the exact passage.
Start freeRelated papers
- Metagenomic biomarker discovery and explanation2011
- MEME SUITE: tools for motif discovery and searching2009
- Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy2005
- The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest2022
- An “Electronic Fluorescent Pictograph” Browser for Exploring and Analyzing Large-Scale Biological Data Sets2007
Metadata from OpenAlex (CC0). Citations are generated from the published record.