Robust Non-Negative Least Squares Using Sparsity
Elvander, Filip LU ; Adalbjörnsson, Stefan Ingi LU and Jakobsson, Andreas LU
(2016)
24th European Signal Processing Conference, EUSIPCO 2016
In European Signal Processing Conference (EUSIPCO)
p.61-65
- Abstract
- Sparse, non-negative signals occur in many applications. To recover such signals, estimation posed as non-negative least squares problems have proven to be fruitful. Efficient algorithms with high accuracy have been proposed, but many of them assume either perfect knowledge of the dictionary generating the signal, or attempts to explain deviations from this dictionary by attributing them to components that for some reason is missing from the dictionary. In this work, we propose a robust non-negative least squares algorithm that allows the generating dictionary to differ from the assumed dictionary, introducing uncertainty in the setup. The proposed algorithm enables an improved modeling of the measurements, and may be efficiently... (More)
- Sparse, non-negative signals occur in many applications. To recover such signals, estimation posed as non-negative least squares problems have proven to be fruitful. Efficient algorithms with high accuracy have been proposed, but many of them assume either perfect knowledge of the dictionary generating the signal, or attempts to explain deviations from this dictionary by attributing them to components that for some reason is missing from the dictionary. In this work, we propose a robust non-negative least squares algorithm that allows the generating dictionary to differ from the assumed dictionary, introducing uncertainty in the setup. The proposed algorithm enables an improved modeling of the measurements, and may be efficiently implemented using a proposed ADMM implementation. Numerical examples illustrate the improved performance as compared to the standard non-negative LASSO estimator. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ab9f9e09-e589-4045-9862-236fe7b36a66
- author
- Elvander, Filip
LU
; Adalbjörnsson, Stefan Ingi
LU
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2016-12-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2016 24th European Signal Processing Conference (EUSIPCO)
- series title
- European Signal Processing Conference (EUSIPCO)
- pages
- 5 pages
- publisher
- EURASIP
- conference name
- 24th European Signal Processing Conference, EUSIPCO 2016
- conference location
- Budapest, Hungary
- conference dates
- 2016-08-28 - 2016-09-02
- external identifiers
-
- scopus:85006054054
- ISSN
- 2076-1465
- ISBN
- 978-0-9928-6265-7
- DOI
- 10.1109/EUSIPCO.2016.7760210
- language
- English
- LU publication?
- yes
- id
- ab9f9e09-e589-4045-9862-236fe7b36a66
- date added to LUP
- 2016-09-22 18:47:18
- date last changed
- 2022-01-30 06:13:41
@inproceedings{ab9f9e09-e589-4045-9862-236fe7b36a66,
abstract = {{Sparse, non-negative signals occur in many applications. To recover such signals, estimation posed as non-negative least squares problems have proven to be fruitful. Efficient algorithms with high accuracy have been proposed, but many of them assume either perfect knowledge of the dictionary generating the signal, or attempts to explain deviations from this dictionary by attributing them to components that for some reason is missing from the dictionary. In this work, we propose a robust non-negative least squares algorithm that allows the generating dictionary to differ from the assumed dictionary, introducing uncertainty in the setup. The proposed algorithm enables an improved modeling of the measurements, and may be efficiently implemented using a proposed ADMM implementation. Numerical examples illustrate the improved performance as compared to the standard non-negative LASSO estimator.}},
author = {{Elvander, Filip and Adalbjörnsson, Stefan Ingi and Jakobsson, Andreas}},
booktitle = {{2016 24th European Signal Processing Conference (EUSIPCO)}},
isbn = {{978-0-9928-6265-7}},
issn = {{2076-1465}},
language = {{eng}},
month = {{12}},
pages = {{61--65}},
publisher = {{EURASIP}},
series = {{European Signal Processing Conference (EUSIPCO)}},
title = {{Robust Non-Negative Least Squares Using Sparsity}},
url = {{http://dx.doi.org/10.1109/EUSIPCO.2016.7760210}},
doi = {{10.1109/EUSIPCO.2016.7760210}},
year = {{2016}},
}