Sparse Prediction with the $k$-Support Norm

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Andreas Argyriou, Rina Foygel, Nathan Srebro


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 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. But through studying our new norm, we also bound the looseness of the elastic net, thus shedding new light on it and providing justification for its use.