Exploiting covariate similarity in sparse regression via the Pairwise Elastic Net

Alexander Lorbert, David Eis, Victoria Kostina, David M. Blei, Peter J. Ramadge

Research output: Contribution to journalConference articlepeer-review

22 Scopus citations


A new approach to regression regularization called the Pairwise Elastic Net is proposed. Like the Elastic Net, it simultaneously performs automatic variable selection and continuous shrinkage. In addition, the Pairwise Elastic Net encourages the grouping of strongly correlated predictors based on a pairwise similarity measure. We give examples of how the approach can be used to achieve the objectives of Ridge regression, the Lasso, the Elastic Net, and Group Lasso. Finally, we present a coordinate descent algorithm to solve the Pairwise Elastic Net.

Original languageEnglish (US)
Pages (from-to)477-484
Number of pages8
JournalJournal of Machine Learning Research
StatePublished - Dec 1 2010
Event13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010 - Sardinia, Italy
Duration: May 13 2010May 15 2010

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


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