Generalised Procrustes Analysis with optimal scaling: Exploring data from a power supplier

Jaap Wieringa, Garmt Dijksterhuis*, John Gower, Frederieke van Perlo

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Generalised Procrustes Analysis (GPA) is a method for matching several, possibly large, data sets by fitting them to each other using transformations, typically rotations. The linear version of GPA has been applied in a wide range of contexts. A non-linear extension of GPA is developed which uses Optimal Scaling (OS). The approach is suited to match data sets that contain nominal variables. A database of a Dutch power supplier that contains many categorical variables unfit for the usual linear GPA methodology is used to illustrate the approach.

Original languageEnglish
Pages (from-to)4546-4554
Number of pages9
JournalComputational Statistics and Data Analysis
Volume53
Issue number12
DOIs
Publication statusPublished - 1 Oct 2009
Externally publishedYes

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