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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.
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Preprints, Working Papers, ...
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Contributor : Puneet Kumar Dokania <>
Submitted on : Friday, August 30, 2013 - 12:02:29 PM
Last modification on : Thursday, February 25, 2021 - 11:28:02 AM

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  • HAL Id : hal-00855999, version 1
  • ARXIV : 1204.5043



Andreas Argyriou, Rina Foygel, Nathan Srebro. Sparse Prediction with the $k$-Support Norm. 2012. ⟨hal-00855999⟩



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