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Arrow Diagrams for Kernel Principal Component Analysis

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dc.contributor.author허명회-
dc.date.accessioned2021-09-06T08:55:01Z-
dc.date.available2021-09-06T08:55:01Z-
dc.date.created2021-06-17-
dc.date.issued2013-
dc.identifier.issn2287-7843-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/105560-
dc.description.abstractKernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis. In addition, we propose an index for individual variables to measure the importance in the principal component plane.-
dc.languageEnglish-
dc.language.isoen-
dc.publisher한국통계학회-
dc.titleArrow Diagrams for Kernel Principal Component Analysis-
dc.title.alternativeArrow Diagrams for Kernel Principal Component Analysis-
dc.typeArticle-
dc.contributor.affiliatedAuthor허명회-
dc.identifier.bibliographicCitationCommunications for Statistical Applications and Methods, v.20, no.3, pp.175 - 184-
dc.relation.isPartOfCommunications for Statistical Applications and Methods-
dc.citation.titleCommunications for Statistical Applications and Methods-
dc.citation.volume20-
dc.citation.number3-
dc.citation.startPage175-
dc.citation.endPage184-
dc.type.rimsART-
dc.identifier.kciidART001771436-
dc.description.journalClass2-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorPrincipal component analysis-
dc.subject.keywordAuthorkernel method-
dc.subject.keywordAuthorradial basis function-
dc.subject.keywordAuthorbiplot-
dc.subject.keywordAuthorarrow diagram.-
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