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dc.contributor.authorLandqvist, R.
dc.contributor.authorMohammed, R.
dc.date.accessioned2016-04-22T06:16:01Z
dc.date.available2016-04-22T06:16:01Z
dc.date.issued2006-12cs
dc.identifier.citationRadioengineering. 2006, vol. 15, č. 4, s. 84-90. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/57980
dc.description.abstractPrincipal Component Analysis (PCA) is an important concept in statistical signal processing. In this paper, we evaluate an on-line algorithm for PCA, which we denote as the Exact Eigendecomposition (EE) algorithm. The algorithm is evaluated using Monte Carlo Simulations and compared with the PAST and RP algorithms. In addition, we investigate a normalization procedure of the eigenvectors for PAST and RP. The results show that EE has the best performance and that normalization improves the performance of PAST and RP algorithms, respectively.en
dc.formattextcs
dc.format.extent84-90cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2006/06_04_84_90.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectPrincipal component analysisen
dc.subjectsignal processingen
dc.subjectexact eigen-decompositionen
dc.titleComparative Performance Analysis of Three Algorithms for Principal Component Analysisen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue4cs
dc.coverage.volume15cs
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Except where otherwise noted, this item's license is described as Creative Commons Attribution 3.0 Unported License