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Optimization with Sparsity-Inducing Penalties
[article]
2011
arXiv
pre-print
Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast as convex optimization problems by regularizing the empirical risk with appropriate non-smooth norms. The goal of this paper is to present from a general perspective optimization
arXiv:1108.0775v2
fatcat:ojhawgf3pnadfdiqk747gi25ry