Non-convex Regularizations for Feature Selection in Ranking With Sparse SVM

International audienceFeature selection in learning to rank has recently emerged as a crucial issue. Whereas several preprocessing approaches have been proposed, only a few works have been focused on integrating the feature selection into the learning process. In this work, we propose a general framework for feature selection in learning to rank using SVM with a sparse regularization term. We investigate both classical convex regularizations such as $\ell_1$ or weighted $\ell_1$ and non-convex regularization terms such as log penalty, Minimax Concave Penalty (MCP) or $\ell_p$ pseudo norm with $p