Abstract
We address the problem of analyzing one or several blocks of variables measured on the same individuals which are a priori divided into several groups. In this framework, we focus on the within-group analysis. For the case of a single dataset, we consider multigroup Principal Component Analysis proposed by several authors (Levin [18]; Krzanowski [16]; Kiers and Ten Berge [13]). A new optimization criterion which characterizes this method and an extension to the case of multiblock datasets are presented. The method is illustrated on the basis of a dataset pertaining to sensory analysis.
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