Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1187
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dc.contributorDepartment of Computing-
dc.creatorYang, J-
dc.creatorZhang, DD-
dc.creatorJin, Z-
dc.creatorYang, JY-
dc.date.accessioned2014-12-11T08:23:29Z-
dc.date.available2014-12-11T08:23:29Z-
dc.identifier.isbn0-7695-2521-0-
dc.identifier.urihttp://hdl.handle.net/10397/1187-
dc.language.isoenen_US
dc.publisherIEEE Computer Societyen_US
dc.rights© 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectDatabase systemsen_US
dc.subjectFeature extractionen_US
dc.subjectLinear programmingen_US
dc.subjectPrincipal component analysisen_US
dc.subjectProblem solvingen_US
dc.titleUnsupervised discriminant projection analysis for feature extractionen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: David Zhangen_US
dc.description.otherinformationBiometrics Centre, Department of Computingen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractThis paper develops an unsupervised discriminant projection (UDP) technique for feature extraction. UDP takes the local and non-local information into account, seeking to find a projection that maximizes the non-local scatter and minimizes the local scatter simultaneously. This characteristic makes UDP more intuitive and more powerful than the up-to-date method - Locality preserving projection (LPP, which considers the local information only) for classification tasks. The proposed method is applied to face biometrics and examined using the ORL and FERET face image databases. Our experimental results show that UDP consistently outperforms LPP, PCA, and LDA.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationThe 18th International Conference on Pattern Recognition : 20-24 August, 2006, Hong Kong : proceedings, v. 1, p. 904-907-
dcterms.issued2006-
dc.identifier.isiWOS:000240678200218-
dc.identifier.scopus2-s2.0-34047237342-
dc.identifier.rosgroupidr30998-
dc.description.ros2006-2007 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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