Geometric properties of partial least squares for process monitoring

Gang LI, S. Joe QIN*, Donghua ZHOU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

340 Citations (Scopus)

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. © 2009 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)204-210
Number of pages7
JournalAutomatica
Volume46
Issue number1
Early online date18 Nov 2009
DOIs
Publication statusPublished - Jan 2010
Externally publishedYes

Keywords

  • Partial least squares (PLS)
  • Process monitoring
  • Simplified PLS (SIMPLS)
  • Weight-deflated PLS (W-PLS)

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