Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloImproved iterative shrinkage-thresholding for sparse signal recovery via Laplace mixtures models
Anno di pubblicazione2018
Autore/iRavazzi, Chiara; Magli, Enrico
Affiliazioni autoriConsiglio Nazionale delle Ricerche (CNR); Politecnico di Torino
Autori CNR e affiliazioni
  • inglese
AbstractIn this paper, we propose a new method for support detection and estimation of sparse and approximately sparse signals from compressed measurements. Using a double Laplace mixture model as the parametric representation of the signal coefficients, the problem is formulated as a weighted l(1) minimization. Then, we introduce a new family of iterative shrinkage-thresholding algorithms based on double Laplace mixture models. They preserve the computational simplicity of classical ones and improve iterative estimation by incorporating soft support detection. In particular, at each iteration, by learning the components that are likely to be nonzero from the current MAP signal estimate, the shrinkage-thresholding step is adaptively tuned and optimized. Unlike other adaptive methods, we are able to prove, under suitable conditions, the convergence of the proposed methods to a local minimum of the weighted l(1) minimization. Moreover, we also provide an upper bound on the reconstruction error. Finally, we show through numerical experiments that the proposed methods outperform classical shrinkage-thresholding in terms of rate of convergence, accuracy, and of sparsity-undersampling trade-off.
Lingua abstractinglese
Altro abstract-
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Pagine da1
Pagine a26
Pagine totali26
RivistaEURASIP Journal on Advances in Signal Processing (Online)
Attiva dal 2007
Editore: Hindawi Publishing Corporation - Cairo
Paese di pubblicazione: Egitto
Lingua: inglese
ISSN: 1687-6180
Titolo chiave: EURASIP Journal on Advances in Signal Processing (Online)
Titolo proprio: EURASIP Journal on Advances in Signal Processing (Online)
Titolo abbreviato: EURASIP J. Adv. Signal Process. (Online)
Numero volume della rivista-
Fascicolo della rivista-
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000438637200002)
Parole chiaveCompressed sensing, Sparse recovery, Gaussian mixture models, MAP estimation, Mixture models, Reweighted l(1) minimization
Link (URL, URI)-
Titolo parallelo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli CNR
    Progetti Europei-
    • JASP2018