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Unbiased Group-Sparsity Sensing Using Quadratic Envelopes

Carlsson, Marcus LU ; Tourneret, Jean Yves and Wendt, Herwig (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:
author
; and
organization
publishing date
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}},
}