%0 Conference Proceedings %T Unbiased Group-Sparsity Sensing Using Quadratic Envelopes %+ Department of Mathematics [Lund University] %+ CoMputational imagINg anD viSion (IRIT-MINDS) %+ Centre National de la Recherche Scientifique (CNRS) %A Carlsson, Marcus %A Tourneret, Jean-Yves %A Wendt, Herwig %< avec comité de lecture %( Proceedings of CAMSAP 2019 %B IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2019) %C Le Gosier ;Guadeloupe, France %3 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) %P 425-429 %8 2019-12-15 %D 2019 %R 10.1109/CAMSAP45676.2019.9022465 %K Sparse representations %K group-sparsity %K quadratic envelope regularization %K proximal operators %Z Computer Science [cs]/Signal and Image ProcessingConference papers %X 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. %G English %2 https://cnrs.hal.science/hal-03034133/document %2 https://cnrs.hal.science/hal-03034133/file/CarlssonCAMSAP2019.pdf %L hal-03034133 %U https://cnrs.hal.science/hal-03034133 %~ UNIV-TLSE2 %~ UNIV-TLSE3 %~ CNRS %~ SMS %~ IRIT %~ IRIT-MINDS %~ IRIT-SI %~ IRIT-CNRS %~ TOULOUSE-INP %~ UNIV-UT3 %~ UT3-INP %~ UT3-TOULOUSEINP