Nonconvex Regularizations for Feature Selection in Ranking With Sparse SVM

Feature selection in learning to rank has recently emerged as a crucial issue. Whereas several preprocessing approaches have been proposed, only a few have focused on integrating feature selection into the learning process. In this paper, we propose a general framework for feature selection in learning to rank using support vector machines with a sparse regularization term. We investigate both classical convex regularizations, such as ℓ 1 or weighted ℓ 1 , and nonconvex regularization terms, such as log penalty, minimax concave penalty, or ℓ p pseudo-norm with p