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Research Article
Generalized PLS regression
Article first published online: 6 FEB 2001
DOI: 10.1002/cem.605
Copyright © 2001 John Wiley & Sons, Ltd.
1099-128X/asset/cover.gif?v=1&s=2e3045c3733baa4258989f44bd61b29dd74ee736 "Journal of Chemometrics")
Additional Information
How to Cite
Xu, Q.-S., Liang, Y.-Z. and Shen, H.-L. (2001), Generalized PLS regression. Journal of Chemometrics, 15: 135–148. doi: 10.1002/cem.605
Publication History
- Issue published online: 6 FEB 2001
- Article first published online: 6 FEB 2001
- Manuscript Accepted: 9 MAR 2000
- Manuscript Received: 15 DEC 1998
Funded by
- National Natural Science Foundation of the People's Republic of China
- Abstract
- References
- Cited By
Keywords:
- generalized partial least squares;
- ridge partial least squares regression;
- generalized ridge partial least squares regression;
- prediction residual error sum of squares;
- mean squared error sum of prediction
Abstract
The present paper develops a class of generalized partial least squares (GPLS) regression methods. GPLS can be regarded as a kind of weighted partial least squares regression method. Two special cases of them, ridge partial least squares (RPLS) and generalized ridge partial least squares (GRPLS) regression methods, are discussed in detail. RPLS and GRPLS combine partial least squares (PLS) with ridge regression (RR) and generalized ridge regression (GRR) respectively. It is shown that the estimated coefficient vectors by RPLS and GRPLS are shrunken PLS estimators and their prediction power is not so sensitive to the components included in the model compared to PLS. The four methods RR, PLS, RPLS and GRPLS are compared on the basis of three data sets under the criteria of prediction residual error sum of squares (PRESS) and mean squared error of prediction (MSEP). Copyright © 2001 John Wiley & Sons, Ltd.