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.

Viewing alternatives

Download history

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

Recommendations