Sparse Prediction with the $k$-Support Norm

Abstract

We derive a novel norm that corresponds to the tightest convex relaxation of sparsity combined with an ${\ell }_2$ penalty. We show that this new {\em $k$-support norm} provides a tighter relaxation than the elastic net and is thus a good replacement for the Lasso or the elastic net in sparse prediction problems. Through the study of the $k$-support norm, we also bound the looseness of the elastic net, thus shedding new light on it and providing justification for its use.