Compressed sensing with preconditioning for sparse recovery with subsampled matrices of Slepian prolate functions (Articolo in rivista)

Type
Label
  • Compressed sensing with preconditioning for sparse recovery with subsampled matrices of Slepian prolate functions (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/s11565-012-0159-3 (literal)
Alternative label
  • Laurent Gosse (2013)
    Compressed sensing with preconditioning for sparse recovery with subsampled matrices of Slepian prolate functions
    in Annali dell'Università di Ferrara. Sezione 7: Scienze matematiche
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Laurent Gosse (literal)
Pagina inizio
  • 81 (literal)
Pagina fine
  • 116 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 59 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • IAC \"Mauro Picone\" (literal)
Titolo
  • Compressed sensing with preconditioning for sparse recovery with subsampled matrices of Slepian prolate functions (literal)
Abstract
  • Efficient recovery of smooth functions which are s-sparse with respect to the basis of so-called prolate spheroidal wave functions from a small number of random sampling points is considered. The main ingredient in the design of both the algorithms we propose here consists in establishing a uniform L? bound on the measurement ensembles which constitute the columns of the sensingmatrix. Such a bound provides us with the restricted isometry property for this rectangular random matrix, which leads to either the exact recovery property or the \"best s-term approximation\" of the original signal by means of the ?1 minimization program. The first algorithm considers only a restricted number of columns for which the L? holds as a consequence of the fact that eigenvalues of the Bergman's restriction operator are close to 1 whereas the second one allows for a wider system of PSWF by taking advantage of a preconditioning technique. Numerical examples are spread throughout the text to illustrate the results. (literal)
Prodotto di
Autore CNR

Incoming links:


Prodotto
Autore CNR di
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
data.CNR.it