Radial basis functions for exploratory data analysis: An iterative majorisation approach for Minkowski distances based on multidimensional scaling

DOI10.1007/s003579900012zbMath0891.92031OpenAlexW1995290486WikidataQ127294405 ScholiaQ127294405MaRDI QIDQ1378869

Andrew R. Webb

Publication date: 18 June 1998

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s003579900012

zbMATH Keywords

feature extractionradial basis functionsnonlinear transformationexploratory data analysismultidimensional scaling\(K\)-means clusteringnonlinear principal componentsiterative majorisationMinkowski distances

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Clustering in the social and behavioral sciences (91C20) One- and multidimensional scaling in the social and behavioral sciences (91C15)

Uses Software

• AS 136

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• A generalized majorization method for least squares multidimensional scaling of pseudodistances that may be negative
• Was Euclid an unnecessarily sophisticated psychologist?
• Monotone mapping of similarities into a general metric space
• The majorization approach to multidimensional scaling for Minkowski distances
• Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
• Principal coordinates analysis with optimal transformation of the variables: Minimizing the sum of squares of the smallest eigenvalues

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