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Sparse Prediction with the $k$-Support Norm
Andreas Argyriou · Rina Foygel · Nati Srebro

Thu Dec 06 12:00 PM -- 12:04 PM (PST) @ Harveys Convention Center Floor, CC
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.

Author Information

Andreas Argyriou (Ecole Centrale de Paris)
Rina Foygel (Stanford University)
Nati Srebro (TTI-Chicago)

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