## An Interior-Point Method for Large-Scale l1-Regularized Logistic RegressionK. Koh, S.-J. Kim, and S. Boyd
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 ( |