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Unbiased Group-Sparsity Sensing Using Quadratic Envelopes

Abstract : This paper investigates a new regularization of the group-sparsity estimation problem based on a quadratic envelope operator. The resulting estimator is shown to have a reduced bias when compared to the classical LASSO estimator and is characterized by a simple hyperparameter selection. Numerical results show that the quadratic envelope regularization yields estimates equal to an oracle solution with high probability. The robustness of the proposed hyperparameter selection rule is also analyzed.
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https://hal-cnrs.archives-ouvertes.fr/hal-03034133
Contributor : Herwig Wendt <>
Submitted on : Tuesday, December 1, 2020 - 4:12:43 PM
Last modification on : Thursday, March 18, 2021 - 2:16:10 PM
Long-term archiving on: : Tuesday, March 2, 2021 - 7:54:09 PM

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Marcus Carlsson, Jean-Yves Tourneret, Herwig Wendt. Unbiased Group-Sparsity Sensing Using Quadratic Envelopes. IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2019), Dec 2019, Le Gosier ;Guadeloupe, France. pp.425-429, ⟨10.1109/CAMSAP45676.2019.9022465⟩. ⟨hal-03034133⟩

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