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dc.contributor.authorWang, Junhui
dc.contributor.authorWang, Lifeng
dc.date.accessioned2011-05-26T04:22:29Z
dc.date.available2011-05-26T04:22:29Z
dc.date.issued2010
dc.identifier.bibliographicCitationWang, J. H. & Wang, L. F. 2010. Sparse supervised dimension reduction in high dimensional classification. Electronic Journal of Statistics, 4: 914-931. DOI: 10.1214/10-EJS572.en
dc.identifier.issn1935-7524
dc.identifier.otherDOI: 10.1214/10-EJS572
dc.identifier.urihttp://hdl.handle.net/10027/7683
dc.descriptionThe original source for this publication is at the Institute of Mathematical Statistics; DOI: 10.1214/10-EJS572en
dc.description.abstractSupervised dimension reduction has proven effective in analyzing data with complex structure. The primary goal is to seek the reduced subspace of minimal dimension which is sufficient for summarizing the data structure of interest. This paper investigates the supervised dimension reduction in high dimensional classification context, and proposes a novel method for estimating the dimension reduction subspace while retaining the ideal classification boundary based on the original dataset. The proposed method combines the techniques of margin based classification and shrinkage estimation, and can estimate the dimension and the directions of the reduced subspace simultaneously. Both theoretical and numerical results indicate that the proposed method is highly competitive against its competitors, especially when the dimension of the covariates exceeds the sample size.en
dc.description.sponsorshipThe research of the second author was supported by a NSF grant DMS-1007634.en
dc.language.isoen_USen
dc.publisherInstitute of Mathematical Statisticsen
dc.subjectdimensionen
dc.subjectsubspaceen
dc.titleSparse supervised dimension reduction in high dimensional classificationen
dc.typeArticleen


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