|Title||Generalised Procrustes Analysis with optimal scaling: Exploring data from a power supplier|
|Author(s)||Wieringa, Jaap; Dijksterhuis, Garmt; Gower, John; Perlo, Frederieke van|
|Source||Computational Statistics & Data Analysis 53 (2009)12. - ISSN 0167-9473 - p. 4546 - 4554.|
|Publication type||Refereed Article in a scientific journal|
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.