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Geometric properties of partial least squares for process monitoring
- Source :
-
Automatica . Jan2010, Vol. 46 Issue 1, p204-210. 7p. - Publication Year :
- 2010
-
Abstract
- 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 00051098
- Volume :
- 46
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Automatica
- Publication Type :
- Academic Journal
- Accession number :
- 47464403
- Full Text :
- https://doi.org/10.1016/j.automatica.2009.10.030