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Titolo:
Backward sequential elimination for sparse vector subset selection
Autore:
Cotter, SF; Kreutz-Delgado, 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:
0165-1684(200109)81:9<1849:BSEFSV>2.0.ZU;2-9
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: Kreutz-Delgado, 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. 1849-1864

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 NP-hard 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 entropy-based and p-norm 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 test-case system., we are able to conclude that the p-norm. 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 test-case dictionary. (C) 2001 Elsevier Science B.V. All rights reserved.

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Documento generato il 07/04/20 alle ore 22:36:38