Robust non-negative least squares using sparsity

Filip Elvander, Stefan Ingi Adalbjörnsson, Andreas Jakobsson

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

Abstrakti

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.
AlkuperäiskieliEnglanti
Otsikko2016 24th European Signal Processing Conference (EUSIPCO)
KustantajaIEEE
Sivut61-65
Sivumäärä5
ISBN (elektroninen)978-0-9928-6265-7
ISBN (painettu)978-1-5090-1891-8
DOI - pysyväislinkit
TilaJulkaistu - 2 syysk. 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEuropean Signal Processing Conference - Budapest, Unkari
Kesto: 28 elok. 20162 syysk. 2016
Konferenssinumero: 24
http://www.eusipco2016.org/

Julkaisusarja

NimiEuropean Signal Processing Conference
ISSN (painettu)2219-5491
ISSN (elektroninen)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
LyhennettäEUSIPCO
Maa/AlueUnkari
KaupunkiBudapest
Ajanjakso28/08/201602/09/2016
www-osoite

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