A Synthesis of canonical variate analysis, generalised canonical correlation and Procrustes analysis

Gardner, Sugnet; Gower, John C. and le Roux, N.J. (2006). A Synthesis of canonical variate analysis, generalised canonical correlation and Procrustes analysis. Computational Statistics and Data Analysis, 50(1) pp. 107–134.

DOI: https://doi.org/10.1016/j.csda.2004.07.020

Abstract

Canonical variate analysis (CVA) is concerned with the analysis of J classes of samples, all described by the same variables. Generalised canonical correlation analysis (GCCA) is concerned with the analysis of K sets of variables, all describing the same samples. A generalised procrustes analysis context is used for data partitioned into J classes of samples and K sets of variables to explore the links between GCCA and CVA. Biplot methodology is used to exploit the visualisation properties of these techniques. This methodology is illustrated by an example of 1425 samples described by three sets of variables (K = 3), the initial analysis of which suggests a grouping of the samples into four classes (J = 4), followed by subsequent more detailed analyses.

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