2003 | OriginalPaper | Buchkapitel
Multidimensional Scaling
verfasst von : Wolfgang Härdle, Léopold Simar
Erschienen in: Applied Multivariate Statistical Analysis
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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One major aim of multivariate data analysis is dimension reduction. For data measured in Euclidean coordinates, Factor Analysis and Principal Component Analysis are dominantly used tools. In many applied sciences data is recorded as ranked information. For example, in marketing, one may record “product A is better than product B”. High-dimensional observations therefore often have mixed data characteristics and contain relative information (w.r.t. a defined standard) rather than absolute coordinates that would enable us to employ one of the multivariate techniques presented so far.