Sparse Prediction with the k-Overlap Norm

@article{Argyriou2012SparsePW,
  title={Sparse Prediction with the k-Overlap Norm},
  author={Andreas Argyriou and Rina Foygel and Nathan Srebro},
  journal={CoRR},
  year={2012},
  volume={abs/1204.5043}
}
We derive a novel norm that corresponds to the tightest convex relaxation of sparsity combined with an l2 penalty and can also be interpreted as a group Lasso norm with overlaps. We show that this new norm provides a tighter relaxation than the elastic net and suggest using it as a replacement for the Lasso or the elastic net in sparse prediction problems. 

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