Is the 1-norm the best convex sparse regularization?
TRAONMILIN, Yann
Centre National de la Recherche Scientifique [CNRS]
Institut de Mathématiques de Bordeaux [IMB]
Centre National de la Recherche Scientifique [CNRS]
Institut de Mathématiques de Bordeaux [IMB]
VAITER, Samuel
Centre National de la Recherche Scientifique [CNRS]
Institut de Mathématiques de Bourgogne [Dijon] [IMB]
Centre National de la Recherche Scientifique [CNRS]
Institut de Mathématiques de Bourgogne [Dijon] [IMB]
TRAONMILIN, Yann
Centre National de la Recherche Scientifique [CNRS]
Institut de Mathématiques de Bordeaux [IMB]
Centre National de la Recherche Scientifique [CNRS]
Institut de Mathématiques de Bordeaux [IMB]
VAITER, Samuel
Centre National de la Recherche Scientifique [CNRS]
Institut de Mathématiques de Bourgogne [Dijon] [IMB]
< Reduce
Centre National de la Recherche Scientifique [CNRS]
Institut de Mathématiques de Bourgogne [Dijon] [IMB]
Language
en
Communication dans un congrès
This item was published in
iTWIST'18 - international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, 2018-11-21, Marseille. p. 1-11
English Abstract
The 1-norm is a good convex regularization for the recovery of sparse vectors from under-determined linear measurements. No other convex regularization seems to surpass its sparse recovery performance. How can this be ...Read more >
The 1-norm is a good convex regularization for the recovery of sparse vectors from under-determined linear measurements. No other convex regularization seems to surpass its sparse recovery performance. How can this be explained? To answer this question, we define several notions of " best " (convex) regulariza-tion in the context of general low-dimensional recovery and show that indeed the 1-norm is an optimal convex sparse regularization within this framework.Read less <
Origin
Hal imported