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

Wieringa, Jaap; Dijksterhuis, Garmt; Gower, John and van Perlo, Frederieke (2009). Generalised procrustes analysis with optimal scaling: exploring data from a power supplier. Computational Statistics and Data Analysis, 53(12) pp. 4546–4554.

DOI: https://doi.org/10.1016/j.csda.2009.03.017

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.

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