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

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

Computational Statistics & Data Analysis, 2009, vol. 53, issue 12, 4546-4554

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.

Date: 2009
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