Catalogo Articoli (Spogli Riviste)
OPAC HELP
Titolo: Backward sequential elimination for sparse vector subset selection
Autore: Cotter, SF; KreutzDelgado, K; Rao, BD;
 Indirizzi:
 Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA Univ Calif San Diego La Jolla CA USA 92093 p Engn, La Jolla, CA 92093 USA
 Titolo Testata:
 SIGNAL PROCESSING
fascicolo: 9,
volume: 81,
anno: 2001,
pagine: 1849  1864
 SICI:
 01651684(200109)81:9<1849:BSEFSV>2.0.ZU;29
 Fonte:
 ISI
 Lingua:
 ENG
 Soggetto:
 MINIMUM NORM ALGORITHM; SIGNAL RECONSTRUCTION; MATCHING PURSUITS; SYSTEMS; FOCUSS;
 Keywords:
 subset selection; sparsity; backward elimination;
 Tipo documento:
 Article
 Natura:
 Periodico
 Settore Disciplinare:
 Engineering, Computing & Technology
 Citazioni:
 41
 Recensione:
 Indirizzi per estratti:
 Indirizzo: KreutzDelgado, K Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA Univ Calif San Diego La Jolla CA USA 92093 CA 92093 USA



 Citazione:
 S.F. Cotter et al., "Backward sequential elimination for sparse vector subset selection", SIGNAL PROC, 81(9), 2001, pp. 18491864
Abstract
Selection of a subset of vectors from a larger dictionary of vectors arises in a wide variety of application areas. This problem is known to be NPhard and many algorithms have been proposed for the suboptimal solution of this problem. The focus of this paper is the development of a backward sequential elimination algorithm wherein. starting from the full dictionary, elements are deleted until a subset of a desired size is obtained. In contrast to previous formulations, we start with an overcomplete dictionary of vectors which is often the problem faced in a signal representation context. Once enough vectors have been deleted to give a complete system.. the algorithm is modified to allow further deletion of vectors, In addition, the derived algorithm gives access to the coefficients associated with each vector inrepresenting the signal. This allows us to experiment with different criteria, including entropybased and pnorm criteria. for selection of the vector to be deleted in each iteration. There is also the flexibility to combine criteria or to switch between criteria at a given stage of the algorithm,Following a series of simulations on a testcase system., we are able to conclude that the pnorm. close to 1 performs best while the system considered is overcomplete. A minimum representation error criterion gives the bestresults once the system considered becomes undercomplete. The performance of the algorithm is also compared to that of forward selection algorithms on the testcase dictionary. (C) 2001 Elsevier Science B.V. All rights reserved.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 07/04/20 alle ore 22:36:38