Brief paper: Geometric properties of partial least squares for process monitoring

Authors:
Gang Li;S. Joe Qin;Donghua Zhou
Affiliations:
Department of Automation, TNList, Tsinghua University, Beijing 100084, PR China;The Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA and Ming Hsieh Department of Electrical Engineering, Univers ...;Department of Automation, TNList, Tsinghua University, Beijing 100084, PR China
Venue:
Automatica (Journal of IFAC)
Year:
2010

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Abstract

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. In this paper, 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.