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Article: On a sparse component analysis approach to blind source separation

TitleOn a sparse component analysis approach to blind source separation
Authors
Issue Date2006
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2006, v. 3889 LNCS, p. 765-772 How to Cite?
AbstractBlind source separation has found applications in various areas including biomedical signal processing and genomic signal processing. Often, blind source separation is performed via independent component analysis (ICA) under the assumption of mutual independence among source signals. However, in bio-signal and genomic signal processing, the assumption of independence is often untrue, and the performance of the ICA approach is not so good. Much effort has been devoted to searching alternative approaches to blind source separation without the independence assumption. In this paper we present a sparse component analysis method, which exploits the sparseness of the source signals and makes the separated signals as sparse as possible according to a properly defined sparsity function, to reliably extract source signals from their mixtures. Some related theoretical and practical issues are investigated, with support and validation by simulation results. © Springer-Verlag Berlin Heidelberg 2006.
Persistent Identifierhttp://hdl.handle.net/10722/118383
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorChang, Cen_HK
dc.contributor.authorFung, PCWen_HK
dc.contributor.authorHung, YSen_HK
dc.date.accessioned2010-09-26T08:02:49Z-
dc.date.available2010-09-26T08:02:49Z-
dc.date.issued2006en_HK
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2006, v. 3889 LNCS, p. 765-772en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/118383-
dc.description.abstractBlind source separation has found applications in various areas including biomedical signal processing and genomic signal processing. Often, blind source separation is performed via independent component analysis (ICA) under the assumption of mutual independence among source signals. However, in bio-signal and genomic signal processing, the assumption of independence is often untrue, and the performance of the ICA approach is not so good. Much effort has been devoted to searching alternative approaches to blind source separation without the independence assumption. In this paper we present a sparse component analysis method, which exploits the sparseness of the source signals and makes the separated signals as sparse as possible according to a properly defined sparsity function, to reliably extract source signals from their mixtures. Some related theoretical and practical issues are investigated, with support and validation by simulation results. © Springer-Verlag Berlin Heidelberg 2006.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.titleOn a sparse component analysis approach to blind source separationen_HK
dc.typeArticleen_HK
dc.identifier.emailChang, C: cqchang@eee.hku.hken_HK
dc.identifier.emailHung, YS: yshung@hkucc.hku.hken_HK
dc.identifier.authorityChang, C=rp00095en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/11679363_95en_HK
dc.identifier.scopuseid_2-s2.0-33745711485en_HK
dc.identifier.hkuros117228en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33745711485&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume3889 LNCSen_HK
dc.identifier.spage765en_HK
dc.identifier.epage772en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridChang, C=7407033052en_HK
dc.identifier.scopusauthoridFung, PCW=7101613315en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK

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