Geometric properties of partial least squares for process monitoring

Gang LI; S. Joe QIN; Donghua ZHOU

doi:10.1016/j.automatica.2009.10.030

Geometric properties of partial least squares for process monitoring

Gang LI, S. Joe QIN^*, Donghua ZHOU

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

351 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 language	English
Pages (from-to)	204-210
Number of pages	7
Journal	Automatica
Volume	46
Issue number	1
Early online date	18 Nov 2009
DOIs	https://doi.org/10.1016/j.automatica.2009.10.030
Publication status	Published - Jan 2010
Externally published	Yes

Keywords

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

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10.1016/j.automatica.2009.10.030

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title = "Geometric properties of partial least squares for process monitoring",

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. {\textcopyright} 2009 Elsevier Ltd. All rights reserved.",

keywords = "Partial least squares (PLS), Process monitoring, Simplified PLS (SIMPLS), Weight-deflated PLS (W-PLS)",

author = "Gang LI and QIN, {S. Joe} and Donghua ZHOU",

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AU - LI, Gang

AU - QIN, S. Joe

AU - ZHOU, Donghua

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AB - 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.

KW - Partial least squares (PLS)

KW - Process monitoring

KW - Simplified PLS (SIMPLS)

KW - Weight-deflated PLS (W-PLS)

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U2 - 10.1016/j.automatica.2009.10.030

DO - 10.1016/j.automatica.2009.10.030

M3 - Journal Article (refereed)

SN - 0005-1098

VL - 46

SP - 204

EP - 210

JO - Automatica

JF - Automatica

IS - 1

ER -

Geometric properties of partial least squares for process monitoring

Abstract

Keywords

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