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Article: Uncorrelated component analysis for blind source separation

TitleUncorrelated component analysis for blind source separation
Authors
Issue Date1999
PublisherBirkhaeuser Boston. The Journal's web site is located at http://link.springer.de/link/service/journals/00034/
Citation
Circuits, Systems, And Signal Processing, 1999, v. 18 n. 3, p. 225-239 How to Cite?
AbstractThe uncorrelated component analysis (UCA) of a stationary random vector process consists of searching for a linear transformation that minimizes the temporal correlation between its components. Through a general analysis we show that under practically reasonable and mild conditions UCA is a solution for blind source separation. The theorems proposed in this paper for UCA provide useful insights for developing practical algorithms. UCA explores the temporal information of the signals, whereas independent component analysis (ICA) explores the spatial information; thus UCA can be applied for source separation in some cases where ICA cannot. For blind source separation, combining ICA and UCA may give improved performance because more information can be utilized. The concept of single UCA (SUCA) is also proposed, which leads to sequential source separation.
Persistent Identifierhttp://hdl.handle.net/10722/155109
ISSN
2015 Impact Factor: 1.178
2015 SCImago Journal Rankings: 0.571
References

 

DC FieldValueLanguage
dc.contributor.authorChang, Cen_HK
dc.contributor.authorYau, SFen_HK
dc.contributor.authorKwok, Pen_HK
dc.contributor.authorChan, FHYen_HK
dc.contributor.authorLam, FKen_HK
dc.date.accessioned2012-08-08T08:31:54Z-
dc.date.available2012-08-08T08:31:54Z-
dc.date.issued1999en_HK
dc.identifier.citationCircuits, Systems, And Signal Processing, 1999, v. 18 n. 3, p. 225-239en_HK
dc.identifier.issn0278-081Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/155109-
dc.description.abstractThe uncorrelated component analysis (UCA) of a stationary random vector process consists of searching for a linear transformation that minimizes the temporal correlation between its components. Through a general analysis we show that under practically reasonable and mild conditions UCA is a solution for blind source separation. The theorems proposed in this paper for UCA provide useful insights for developing practical algorithms. UCA explores the temporal information of the signals, whereas independent component analysis (ICA) explores the spatial information; thus UCA can be applied for source separation in some cases where ICA cannot. For blind source separation, combining ICA and UCA may give improved performance because more information can be utilized. The concept of single UCA (SUCA) is also proposed, which leads to sequential source separation.en_HK
dc.languageengen_US
dc.publisherBirkhaeuser Boston. The Journal's web site is located at http://link.springer.de/link/service/journals/00034/en_HK
dc.relation.ispartofCircuits, Systems, and Signal Processingen_HK
dc.titleUncorrelated component analysis for blind source separationen_HK
dc.typeArticleen_HK
dc.identifier.emailChang, C: cqchang@eee.hku.hken_HK
dc.identifier.authorityChang, C=rp00095en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0032677631en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0032677631&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume18en_HK
dc.identifier.issue3en_HK
dc.identifier.spage225en_HK
dc.identifier.epage239en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChang, C=7407033052en_HK
dc.identifier.scopusauthoridYau, SF=7202478362en_HK
dc.identifier.scopusauthoridKwok, P=7101871278en_HK
dc.identifier.scopusauthoridChan, FHY=7202586429en_HK
dc.identifier.scopusauthoridLam, FK=7102075939en_HK

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