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**An interior-point method for large-scale \(l_1\)-regularized logistic regression.**
*(English)*
Zbl 1222.62092

Summary: Logistic regression with \(l_1\) regularization has been proposed as a promising method for feature selection in classification problems. We describe an efficient interior-point method for solving large-scale \(l_1\)-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.

### MSC:

62J12 | Generalized linear models (logistic models) |

65C60 | Computational problems in statistics (MSC2010) |