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An interior-point method for large-scale l1-regularized logistic regression (2007)
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| Venue: | Journal of Machine Learning Research |
| Citations: | 290 - 9 self |
BibTeX
@ARTICLE{Koh07aninterior-point,
author = {Kwangmoo Koh and Seung-jean Kim and Stephen Boyd and Yi Lin},
title = {An interior-point method for large-scale l1-regularized logistic regression},
journal = {Journal of Machine Learning Research},
year = {2007},
volume = {2007}
}
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Abstract
Logistic regression with ℓ1 regularization has been proposed as a promising method for feature selection in classification problems. In this paper we describe an efficient interior-point method for solving large-scale ℓ1-regularized logistic regression problems. Small problems with up to a thousand or so features and examples can be solved in seconds on a PC; medium sized problems, with tens of thousands of features and examples, can be solved in tens of seconds (assuming some sparsity in the data). A variation on the basic method, that uses a preconditioned conjugate gradient method to compute the search step, can solve very large problems, with a million features and examples (e.g., the 20 Newsgroups data set), in a few minutes, on a PC. Using warm-start techniques, a good approximation of the entire regularization path can be computed much more efficiently than by solving a family of problems independently.
Keyphrases
large-scale l1-regularized logistic regression interior-point method preconditioned conjugate gradient method promising method classification problem large problem good approximation large-scale 1-regularized logistic regression problem efficient interior-point method entire regularization path warm-start technique newsgroups data set feature selection logistic regression basic method search step
