Abstract
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 language | English (US) |
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Pages (from-to) | 477-484 |
Number of pages | 8 |
Journal | Journal of Machine Learning Research |
Volume | 9 |
State | Published - 2010 |
Event | 13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010 - Sardinia, Italy Duration: May 13 2010 → May 15 2010 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Software
- Statistics and Probability
- Artificial Intelligence