Arrow Diagrams for Kernel Principal Component Analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 허명회 | - |
dc.date.accessioned | 2021-09-06T08:55:01Z | - |
dc.date.available | 2021-09-06T08:55:01Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 2287-7843 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/105560 | - |
dc.description.abstract | Kernel 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.language | English | - |
dc.language.iso | en | - |
dc.publisher | 한국통계학회 | - |
dc.title | Arrow Diagrams for Kernel Principal Component Analysis | - |
dc.title.alternative | Arrow Diagrams for Kernel Principal Component Analysis | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 허명회 | - |
dc.identifier.bibliographicCitation | Communications for Statistical Applications and Methods, v.20, no.3, pp.175 - 184 | - |
dc.relation.isPartOf | Communications for Statistical Applications and Methods | - |
dc.citation.title | Communications for Statistical Applications and Methods | - |
dc.citation.volume | 20 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 175 | - |
dc.citation.endPage | 184 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART001771436 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Principal component analysis | - |
dc.subject.keywordAuthor | kernel method | - |
dc.subject.keywordAuthor | radial basis function | - |
dc.subject.keywordAuthor | biplot | - |
dc.subject.keywordAuthor | arrow diagram. | - |
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