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Probabilistic Principal Curves on Riemannian Manifolds

DC Field Value Language
dc.contributor.authorKang, Seungwoo-
dc.contributor.authorOh, Hee Seok-
dc.date.accessioned2024-07-24T01:05:24Z-
dc.date.available2024-07-24T01:05:24Z-
dc.date.created2024-07-02-
dc.date.issued2024-07-
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.46 No.7, pp.4843-4849-
dc.identifier.issn0162-8828-
dc.identifier.urihttps://hdl.handle.net/10371/204776-
dc.description.abstractThis paper studies a new curve-fitting approach to data on Riemannian manifolds. We define a principal curve based on a mixture model for observations and unobserved latent variables and propose a new algorithm to estimate the principal curve for given data points on Riemannian manifolds.-
dc.language영어-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.titleProbabilistic Principal Curves on Riemannian Manifolds-
dc.typeArticle-
dc.identifier.doi10.1109/TPAMI.2024.3357801-
dc.citation.journaltitleIEEE Transactions on Pattern Analysis and Machine Intelligence-
dc.identifier.wosid001240147800024-
dc.identifier.scopusid2-s2.0-85183986085-
dc.citation.endpage4849-
dc.citation.number7-
dc.citation.startpage4843-
dc.citation.volume46-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorOh, Hee Seok-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordPlusGEOMETRY-
dc.subject.keywordAuthorManifolds-
dc.subject.keywordAuthorProbabilistic logic-
dc.subject.keywordAuthorGaussian distribution-
dc.subject.keywordAuthorFitting-
dc.subject.keywordAuthorPrincipal component analysis-
dc.subject.keywordAuthorWrapping-
dc.subject.keywordAuthorTime series analysis-
dc.subject.keywordAuthorDimensionality reduction-
dc.subject.keywordAuthorEM algorithm-
dc.subject.keywordAuthorprincipal curve-
dc.subject.keywordAuthorRiemannian manifold-
dc.subject.keywordAuthorsymmetric space-
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