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2003 | OriginalPaper | Buchkapitel

Multidimensional Scaling

verfasst von : Wolfgang Härdle, Léopold Simar

Erschienen in: Applied Multivariate Statistical Analysis

Verlag: Springer Berlin Heidelberg

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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.

Metadaten
Titel
Multidimensional Scaling
verfasst von
Wolfgang Härdle
Léopold Simar
Copyright-Jahr
2003
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-662-05802-2_15