2012/04/23 by Andreas Argyriou, Argyriou, Andreas, Rina Foygel +3 · 1 citation
Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1204.5043
arxiv created 2012/06/12 · arxiv updated 2012/06/13
We derive a novel norm that corresponds to the tightest convex relaxation of sparsity combined with an ℓ2 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.