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dc.contributor.authorZhao, L.
dc.contributor.authorJia, W.
dc.contributor.authorWang, R.
dc.contributor.authorYu, Q.
dc.date.accessioned2016-04-20T06:25:36Z
dc.date.available2016-04-20T06:25:36Z
dc.date.issued2016-04cs
dc.identifier.citationRadioengineering. 2016 vol. 25, č. 1, s. 200-207. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/57928
dc.description.abstractThe L1-norm based tensor analysis (TPCA-L1) is recently proposed for dimensionality reduction and feature extraction. However, a greedy strategy was utilized for solving the L1-norm maximization problem, which makes it prone to being stuck in local solutions. In this paper, we propose a robust TPCA with non-greedy L1-norm maximization (TPCA-L1 non-greedy), in which all projection directions are optimized simultaneously. Experiments on several face databases demonstrate the effectiveness of the proposed method.en
dc.formattextcs
dc.format.extent200-207cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2016/16_01_0012_0017.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectPrincipal component analysis (PCA)en
dc.subjectTPCAen
dc.subjectL1-normen
dc.subjectoutliersen
dc.subjectnon-greedy strategyen
dc.titleRobust Tensor Analysis with Non-Greedy L1-Norm Maximizationen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue1cs
dc.coverage.volume25cs
dc.identifier.doi10.13164/re.2016.0200en
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