Generalised procrustes analysis with optimal scaling: Exploring data from a power supplier

J.E. Wieringa, J.C. Gower, G.B. Dijksterhuis, F. van Perlo-ten Kleij

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. (c) 2009 Elsevier B.V. All rights reserved.

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

Cite this