Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24382
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorSong, FX-
dc.creatorZhang, D-
dc.creatorYang, JY-
dc.creatorGao, XM-
dc.date.accessioned2014-12-31T08:01:44Z-
dc.date.available2014-12-31T08:01:44Z-
dc.identifier.issn0254-4156-
dc.identifier.urihttp://hdl.handle.net/10397/24382-
dc.language.isozhen_US
dc.publisher科學出版社en_US
dc.subjectAdaptive algorithmen_US
dc.subjectFace recognitionen_US
dc.subjectFisher discriminant criterionen_US
dc.subjectLarge margin linear projectionen_US
dc.subjectMachine learningen_US
dc.subjectMaximum scatter differenceen_US
dc.titleAdaptive classification algorithm based on maximum scatter difference discriminant criterionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage541-
dc.identifier.epage549-
dc.identifier.volume32-
dc.identifier.issue4-
dcterms.abstractIn this paper we first prove that the optimal discriminant direction of Maximum scatter difference (MSD) discriminant criterion with a certain value c0 is equivalent to the optimal Fisher discriminant direction. Second, sample recognition rate curves of MSD are illustrated. The recognition rate curve is usually a pulse curve when the within-class scatter matrix is nonsingular. With the increase of parameter C, the recognition rate of MSD also increases. The recognition rate of MSD achieves its maximum when C is equal to c0. In addition, former study showed that, when the within-class scatter matrix is singular, MSD criterion is approaching the large margin linear projection criterion as parameter C increases. Moreover, the recognition rate curve of MSD is non-decreasing. Thus, an adaptive classification algorithm based on maximum scatter difference discriminant criterion is proposed based on these facts. The new algorithm can tune parameter C automatically according to the characteristics of training samples. Experiment conducted on 6 datasets from UCI Machine Learning Repository and AR face database demonstrates that the adaptive classification algorithm for maximum scatter difference has good classification property.-
dcterms.bibliographicCitation自动化学报 (Acta automatica sinica), 2006, v. 32, no. 4, p. 541-549-
dcterms.isPartOf自动化学报 (Acta automatica sinica)-
dcterms.issued2006-
dc.identifier.scopus2-s2.0-33748463769-
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