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Regularization paths for generalized linear models via coordinate descent (2009)
| Content Provider | CiteSeerX |
|---|---|
| Author | Hastie, Trevor Tibshirani, Rob Friedman, Jerome |
| Abstract | We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods. |
| File Format | |
| Journal | Journal of Statistical Software |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Subject Keyword | Algorithm Use Cyclical Coordinate Descent Fast Algorithm Elastic Net Convex Penalty Regularization Path Generalized Linear Model Coordinate Descent Multinomial Regression Problem New Algorithm Sparse Feature Comparative Timing Two-class Logistic Regression Large Problem |
| Content Type | Text |
| Resource Type | Article |
