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Geometric properties of partial least squares for process monitoring. (English) Zbl 1233.62208
Summary: Projection to latent structures or partial least squares (PLS) produces output-supervised decomposition on input X, while principal component analysis (PCA) produces unsupervised decomposition of input X. The effect of output Y on the X-space decomposition in PLS is analyzed and geometric properties of the PLS structure are revealed. Several PLS algorithms are compared in a geometric way for the purpose of process monitoring. A numerical example and a case study are given to illustrate the analysis results.
MSC:
62P30Applications of statistics in engineering and industry
62H25Factor analysis and principal components; correspondence analysis
65C60Computational problems in statistics