Linear Discriminant Analysis for Data with Subcluster Structure

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dc.contributor.authorPark, Haesunko
dc.contributor.authorChoo, Jaegulko
dc.contributor.authorDrake, Barry L.ko
dc.contributor.authorKang, Jinwooko
dc.date.accessioned2020-03-25T02:21:12Z-
dc.date.available2020-03-25T02:21:12Z-
dc.date.created2020-03-24-
dc.date.issued2008-12-
dc.identifier.citation19th International Conference on Pattern Recognition (ICPR 2008), pp.579 - +-
dc.identifier.issn1051-4651-
dc.identifier.urihttp://hdl.handle.net/10203/273505-
dc.description.abstractLinear discriminant analysis (LDA) is a widely-used feature extraction method in classification. However the original LDA has limitations due to the assumption of a unimodal structure for each cluster, which is satisfied in many applications such as facial image data when variations such as angle and illumination can significantly influence the images of the same person. In this paper we propose a novel method, hierarchical LDA(h-LDA), which takes into account hierarchical subcluster structures in the data sets. Our experiments show that regularized h-LDA produces better accuracy than LDA, PCA, and tensorFaces.-
dc.languageEnglish-
dc.publisherIEEE Computer Society-
dc.titleLinear Discriminant Analysis for Data with Subcluster Structure-
dc.typeConference-
dc.identifier.wosid000264729000142-
dc.type.rimsCONF-
dc.citation.beginningpage579-
dc.citation.endingpage+-
dc.citation.publicationname19th International Conference on Pattern Recognition (ICPR 2008)-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationTampa, FL-
dc.identifier.doi10.1109/ICPR.2008.4761084-
dc.contributor.nonIdAuthorPark, Haesun-
dc.contributor.nonIdAuthorDrake, Barry L.-
dc.contributor.nonIdAuthorKang, Jinwoo-
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