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Tipo di prodottoArticolo in rivista
TitoloGaussian Mixtures Based IRLS for Sparse Recovery With Quadratic Convergence
Anno di pubblicazione2015
FormatoElettronico
Autore/iRavazzi, Chiara; Magli, Enrico
Affiliazioni autoriPolitecnico di Torino
Autori CNR e affiliazioni
  • CHIARA RAVAZZI
Lingua/e
  • inglese
AbstractIn this paper, we propose a new class of iteratively re-weighted least squares (IRLS) for sparse recovery problems. The proposed methods are inspired by constrained maximum-likelihood estimation under a Gaussian scale mixture (GSM) distribution assumption. In the noise-free setting, we provide sufficient conditions ensuring the convergence of the sequences generated by these algorithms to the set of fixed points of the maps that rule their dynamics and derive conditions verifiable a posteriori for the convergence to a sparse solution. We further prove that these algorithms are quadratically fast in a neighborhood of a sparse solution. We show through numerical experiments that the proposed methods outperform classical IRLS for l(tau)-minimization with tau is an element of (0,1] in terms of speed and of sparsity-undersampling tradeoff and are robust even in presence of noise. The simplicity and the theoretical guarantees provided in this paper make this class of algorithms an attractive solution for sparse recovery problems.
Lingua abstractinglese
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Pagine da3474
Pagine a3489
Pagine totali16
RivistaIEEE transactions on signal processing
Attiva dal 1991
Editore: Institute of Electrical and Electronics Engineers, - New York, NY
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1053-587X
Titolo chiave: IEEE transactions on signal processing
Titolo proprio: IEEE transactions on signal processing
Titolo abbreviato: IEEE trans. signal process.
Titoli alternativi:
  • Institute of Electrical and Electronics Engineers transactions on signal processing
  • Transactions on signal processing
  • Signal processing
Numero volume della rivista63
Fascicolo della rivista13
DOI10.1109/TSP.2015.2428216
Verificato da refereeSì: Internazionale
Stato della pubblicazionePostprint
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000355994200015)
Parole chiaveCompressed sensing, constrained maximum likelihood estimation, Gaussian scale mixtures, iterative support detection and estimation, iteratively re-weighted least squares methods
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Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
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Allegati
  • Gaussian mixtures based IRLS for sparse recovery with quadratic convergence
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