Unbiased Group-Sparsity Sensing Using Quadratic Envelopes
(2019) 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings p.425-429- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d51b9063-d4d5-409e-9d7b-598a75aede93
- author
- Carlsson, Marcus LU ; Tourneret, Jean Yves and Wendt, Herwig
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- group-sparsity, proximal operators, quadratic envelope regularization, Sparse representations
- host publication
- 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
- series title
- 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
- article number
- 9022465
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019
- conference location
- Le Gosier, Guadeloupe
- conference dates
- 2019-12-15 - 2019-12-18
- external identifiers
-
- scopus:85082383103
- ISBN
- 9781728155494
- DOI
- 10.1109/CAMSAP45676.2019.9022465
- language
- English
- LU publication?
- yes
- id
- d51b9063-d4d5-409e-9d7b-598a75aede93
- date added to LUP
- 2020-04-14 13:15:46
- date last changed
- 2022-04-18 21:40:29
@inproceedings{d51b9063-d4d5-409e-9d7b-598a75aede93, abstract = {{<p>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.</p>}}, author = {{Carlsson, Marcus and Tourneret, Jean Yves and Wendt, Herwig}}, booktitle = {{2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings}}, isbn = {{9781728155494}}, keywords = {{group-sparsity; proximal operators; quadratic envelope regularization; Sparse representations}}, language = {{eng}}, pages = {{425--429}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings}}, title = {{Unbiased Group-Sparsity Sensing Using Quadratic Envelopes}}, url = {{http://dx.doi.org/10.1109/CAMSAP45676.2019.9022465}}, doi = {{10.1109/CAMSAP45676.2019.9022465}}, year = {{2019}}, }