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Published in:
2012 | OriginalPaper | Chapter
Multidimensional Scaling
Authors : Wolfgang Karl Härdle, Léopold Simar
Published in: Applied Multivariate Statistical Analysis
Publisher: 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.