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Sparse Prediction with the k-Support Norm

2020-01-13 | |  48 |   39 |   0

Abstract

We derive a novel norm that corresponds to the tightest convex relaxation of sparsity combined with an 图片.png penalty. We show that this new k-support norm provides a tighter relaxation than the elastic net and can thus be advantageous in in sparse prediction problems. 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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