An Efficient Method for Large-Scale l1-Regularized Convex Loss Minimization

Convex loss minimization with l1 regularization has been proposed as a promising method for feature selection in classification (e.g., l1-regularized logistic regression) and regression (e.g., l1-regularized least squares). In this paper we describe an efficient interior-point method for solving large-scale l1-regularized convex loss minimization problems that uses a preconditioned conjugate gradient method to compute the search step. The method can solve very large problems. For example, the method can solve an l1-regularized logistic regression problem with a million features and examples (e.g., the 20 Newsgroups data set), in a few minutes, on a PC.