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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)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
European Signal Processing Conference (EUSIPCO)
pages
5 pages
publisher
EURASIP
conference name
24th European Signal Processing Conference, EUSIPCO 2016
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
2017-03-16 09:33:13
@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    = {European Signal Processing Conference (EUSIPCO)},
  isbn         = {978-0-9928-6265-7},
  issn         = {2076-1465},
  language     = {eng},
  month        = {12},
  pages        = {61--65},
  publisher    = {EURASIP},
  title        = {Robust Non-Negative Least Squares Using Sparsity},
  url          = {http://dx.doi.org/10.1109/EUSIPCO.2016.7760210},
  year         = {2016},
}