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dc.contributor.authorDiday, Edwin*
dc.contributor.authorGroenen, P.J.F.*
dc.contributor.authorWinsberg, Suzanne*
dc.contributor.authorRodriguez, O.*
dc.date.accessioned2009-06-30T10:02:53Z
dc.date.available2009-06-30T10:02:53Z
dc.date.issued2006-01
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/547
dc.language.isoenen
dc.subjectI-scal
dc.subjectIterative majorization
dc.subjectInterval-type data
dc.subjectMultidimensional scalingen
dc.subject.ddc519en
dc.titleI-scal : Multidimensional scaling of interval dissimilaritiesen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherUniversidad de Costa Rica;Costa Rica
dc.contributor.editoruniversityotherErasmus University Rotterdam;Pays-Bas
dc.description.abstractenMultidimensional scaling aims at reconstructing dissimilarities between pairs of objects by distances in a low-dimensional space. However, in some cases the dissimilarity itself is unknown, but the range of the dissimilarity is given. Such fuzzy data give rise to a data matrix in which each dissimilarity is an interval of values. These interval dissimilarities are modelled by the ranges of the distances defined as the minimum and maximum distance between two rectangles representing the objects. Previously, two approaches for such data have been proposed and one of them is investigated. A new algorithm called I-Scal is developed. Because I-Scal is based on iterative majorization it has the advantage that each iteration is guaranteed to improve the solution until no improvement is possible. In addition, a rational start configuration is proposed that is helpful in locating a good quality local minima. In a simulation study, the quality of this algorithm is investigated and I-Scal is compared with one previously proposed algorithm. Finally, I-Scal is applied on an empirical example of dissimilarity intervals of sounds.
dc.relation.isversionofjnlnameComputational Statistics and Data Analysis
dc.relation.isversionofjnlvol51en
dc.relation.isversionofjnlissue1en
dc.relation.isversionofjnldate2006-05
dc.relation.isversionofjnlpages360-378en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.csda.2006.04.003en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherElsevier
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
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