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

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

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.

Suggested Citation

  • Wieringa, Jaap & Dijksterhuis, Garmt & Gower, John & van Perlo, Frederieke, 2009. "Generalised Procrustes Analysis with optimal scaling: Exploring data from a power supplier," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4546-4554, October.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:4546-4554
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    References listed on IDEAS

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    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    2. Blasius, Jörg & Eilers, Paul H.C. & Gower, John, 2009. "Better biplots," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3145-3158, June.
    3. Dijksterhuis, Garmt & Martens, Harald & Martens, Magni, 2005. "Combined Procrustes analysis and PLSR for internal and external mapping of data from multiple sources," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 47-62, January.
    4. Gardner, Sugnet & Gower, John C. & le Roux, N.J., 2006. "A synthesis of canonical variate analysis, generalised canonical correlation and Procrustes analysis," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 107-134, January.
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