Geometric properties of partial least squares for process monitoring
By Gang Li, S. Qin, Donghua Zhou. at - Automatisierungstechnik, 2010
345 citations recorded by the source corpus.
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.
Subfields: Analytical Chemistry, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/d69e03d92592ef264c153b5568ad70afb553b518
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In Analytical Chemistry
In Control and Systems Engineering
Citations in the corpus
- Survey on data-driven industrial process monitoring and diagnosis (2012, methodology): “Recently, Li et al. (2010) develop geometric properties of PLS that are relevant to process monitoring.”
- Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis (2015, methodology): “However, as shown in (5)-(6), standard PLS performs undesired oblique decomposition [19] thus cannot ensure further KPI-related fault diagnosis results.”
- Data-Based Techniques Focused on Modern Industry: An Overview (2015, background): “ful industrial applications reported in recent literature [24]–”