Online Group-Sparse Regression Using the Covariance Fitting Criterion
(2017) In European Signal Processing Conference (EUSIPCO) CFP1740S-USB.- Abstract
- In this paper, we present a time-recursive implementation of a recent hyperparameter-free group-sparse estimation technique. This is achieved byr eformulating the original method, termed group-SPICE, as a square-root group-LASSO with a suitable regularization level, for which a time-recursive implementation is derived. Using a proximal gradient step for lowering the computational cost, the proposed method may effectively cope with data sequences consisting of both stationary and non-stationary signals, such as transients, and/or amplitude modulated signals. Numerical examples illustrates the efficacy of the proposed method for both coherent Gaussian dictionaries and for the multi-pitch estimation problem.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/40f6dfcc-75fe-4384-931d-d17494c3d55e
- author
- Kronvall, Ted LU ; Adalbjörnsson, Stefan Ingi LU ; Nadig, Santhosh and Jakobsson, Andreas LU
- organization
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the 25th European Signal Processing Conference (EUSIPCO)
- series title
- European Signal Processing Conference (EUSIPCO)
- volume
- CFP1740S-USB
- article number
- 1570347373
- pages
- 5 pages
- publisher
- EURASIP
- ISSN
- 2076-1465
- ISBN
- 978-0-9928626-8-8
- language
- English
- LU publication?
- yes
- additional info
- I
- id
- 40f6dfcc-75fe-4384-931d-d17494c3d55e
- alternative location
- http://www.eurasip.org/Proceedings/Eusipco/Eusipco2017/papers/1570347373.pdf
- date added to LUP
- 2017-10-05 14:19:00
- date last changed
- 2019-03-08 02:33:57
@inproceedings{40f6dfcc-75fe-4384-931d-d17494c3d55e, abstract = {{In this paper, we present a time-recursive implementation of a recent hyperparameter-free group-sparse estimation technique. This is achieved byr eformulating the original method, termed group-SPICE, as a square-root group-LASSO with a suitable regularization level, for which a time-recursive implementation is derived. Using a proximal gradient step for lowering the computational cost, the proposed method may effectively cope with data sequences consisting of both stationary and non-stationary signals, such as transients, and/or amplitude modulated signals. Numerical examples illustrates the efficacy of the proposed method for both coherent Gaussian dictionaries and for the multi-pitch estimation problem.}}, author = {{Kronvall, Ted and Adalbjörnsson, Stefan Ingi and Nadig, Santhosh and Jakobsson, Andreas}}, booktitle = {{Proceedings of the 25th European Signal Processing Conference (EUSIPCO)}}, isbn = {{978-0-9928626-8-8}}, issn = {{2076-1465}}, language = {{eng}}, publisher = {{EURASIP}}, series = {{European Signal Processing Conference (EUSIPCO)}}, title = {{Online Group-Sparse Regression Using the Covariance Fitting Criterion}}, url = {{https://lup.lub.lu.se/search/files/32747968/KronvallANJ17_final.pdf}}, volume = {{CFP1740S-USB}}, year = {{2017}}, }