Gardner, Sugnet; Gower, John C. and le Roux, N.J.
|DOI (Digital Object Identifier) Link:||http://dx.doi.org/10.1016/j.csda.2004.07.020|
|Google Scholar:||Look up in Google Scholar|
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.
|Item Type:||Journal Article|
|Keywords:||canonical variate analysis; generalised canonical correlation analysis; generalised Procrustes analysis; biplots; data mining|
|Academic Unit/Department:||Mathematics, Computing and Technology > Mathematics and Statistics
Mathematics, Computing and Technology
|Depositing User:||Heather Whitaker|
|Date Deposited:||19 Jun 2006|
|Last Modified:||14 Jan 2016 15:50|
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