Hyperparameter-free sparse regression of grouped variables
Kronvall, Ted LU ; Adalbjornsson, Stefan Ingi LU ; Nadig, Santhosh and Jakobsson, Andreas LU
(2017)
50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
p.394-398
- Abstract
In this paper, we introduce a novel framework for semi-parametric estimation of an unknown number of signals, each parametrized by a group of components. Via a reformulation of the covariance fitting criteria, we formulate a convex optimization problem over a grid of candidate representations, promoting solutions with only a few active groups. Utilizing the covariance fitting allows for a hyperparameter-free estimation procedure, highly robust against coherency between candidates, while still allowing for a computationally efficient implementation. Numerical simulations illustrate how the proposed method offers a performance similar to the group-LASSO for incoherent dictionaries, and superior performance for coherent dictionaries.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d1c97ff7-ea12-4766-84fb-72369c241aed
- author
- Kronvall, Ted
LU
; Adalbjornsson, Stefan Ingi
LU
; Nadig, Santhosh
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2017-03-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- convex optimization, covariance fitting, group sparsity, multi-pitch estimation
- host publication
- Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
- article number
- 7869067
- pages
- 5 pages
- publisher
- IEEE Computer Society
- conference name
- 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
- conference location
- Pacific Grove, United States
- conference dates
- 2016-11-06 - 2016-11-09
- external identifiers
-
- scopus:85016250766
- ISBN
- 9781538639542
- DOI
- 10.1109/ACSSC.2016.7869067
- language
- English
- LU publication?
- yes
- id
- d1c97ff7-ea12-4766-84fb-72369c241aed
- date added to LUP
- 2017-04-12 15:01:22
- date last changed
- 2022-03-09 02:21:18
@inproceedings{d1c97ff7-ea12-4766-84fb-72369c241aed,
abstract = {{<p>In this paper, we introduce a novel framework for semi-parametric estimation of an unknown number of signals, each parametrized by a group of components. Via a reformulation of the covariance fitting criteria, we formulate a convex optimization problem over a grid of candidate representations, promoting solutions with only a few active groups. Utilizing the covariance fitting allows for a hyperparameter-free estimation procedure, highly robust against coherency between candidates, while still allowing for a computationally efficient implementation. Numerical simulations illustrate how the proposed method offers a performance similar to the group-LASSO for incoherent dictionaries, and superior performance for coherent dictionaries.</p>}},
author = {{Kronvall, Ted and Adalbjornsson, Stefan Ingi and Nadig, Santhosh and Jakobsson, Andreas}},
booktitle = {{Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016}},
isbn = {{9781538639542}},
keywords = {{convex optimization; covariance fitting; group sparsity; multi-pitch estimation}},
language = {{eng}},
month = {{03}},
pages = {{394--398}},
publisher = {{IEEE Computer Society}},
title = {{Hyperparameter-free sparse regression of grouped variables}},
url = {{http://dx.doi.org/10.1109/ACSSC.2016.7869067}},
doi = {{10.1109/ACSSC.2016.7869067}},
year = {{2017}},
}