کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1180908 | 1491545 | 2014 | 7 صفحه PDF | دانلود رایگان |
• We outline new properties of multigroup Principal Component Analysis.
• We extend this method of analysis to multiblock and multigroup data.
• We illustrate the proposed method on real dataset.
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.
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 133, 15 April 2014, Pages 63–69