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dc.contributor.authorEinbeck, Jochen
dc.contributor.authorTutz, Gerhard
dc.contributor.authorEvers, Ludger
dc.date.accessioned2018-08-24T08:24:41Z
dc.date.available2018-08-24T08:24:41Z
dc.date.issued2005-10-01
dc.identifier.citationEinbeck, Jochen; Tutz, Gerhard; Evers, Ludger (2005). Local principal curves. Statistics and Computing 15 (4), 301-313
dc.identifier.issn0960-3174,1573-1375
dc.identifier.urihttp://hdl.handle.net/10379/9102
dc.description.abstractPrincipal components are a well established tool in dimension reduction. The extension to principal curves allows for general smooth curves which pass through the middle of a multidimensional data cloud. In this paper local principal curves are introduced, which are based on the localization of principal component analysis. The proposed algorithm is able to identify closed curves as well as multiple curves which may or may not be connected. For the evaluation of the performance of principal curves as tool for data reduction a measure of coverage is suggested. By use of simulated and real data sets the approach is compared to various alternative concepts of principal curves.
dc.publisherSpringer Nature
dc.relation.ispartofStatistics and Computing
dc.subjectlocal smoothing
dc.subjectmean shift
dc.subjectprincipal components
dc.subjectprincipal curves
dc.subjectcomponent analysis
dc.titleLocal principal curves
dc.typeArticle
dc.identifier.doi10.1007/s11222-005-4073-8
dc.local.publishedsourcehttp://eprints.gla.ac.uk/45525/1/45525.pdf
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