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dc.contributor.authorErgon, Rolf
dc.date.accessioned2007-02-08T09:44:55Z
dc.date.accessioned2017-04-19T12:49:52Z
dc.date.available2007-02-08T09:44:55Z
dc.date.available2017-04-19T12:49:52Z
dc.date.issued2003
dc.identifier.citationChemometrics and intelligent laboratory systems 65 (2003), No. 2, p. 293-303
dc.identifier.issn0169-7439
dc.identifier.urihttp://hdl.handle.net/11250/2438417
dc.description.abstractAssuming a fully known latent variables (LV) model, the optimal multivariate calibration predictor is found from Kalman filtering theory. From this follows the best possible column space for a loading weight matrix Wopt. in a predictor based on the latent variables, and thus the optimal factorization of the regressor matrix X. Although the optimal predictor cannot be directly determined in a practical case, we may still make an attempt to find it. The paper presents a simple algorithm for a constrained numerical search for a Wopt. matrix spanning the optimal column space, using a principal component analysis (PCR) or a partial least squares (PLS) factorization as a starting point. The constraint is necessary in order to avoid overfitting, and it is based on an assumption of a smooth predictor. A simulation example and data from a metal ion mixture experiment are used to demonstrate the feasibility of the proposed method.
dc.format.extent704235 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.subjectPCR/PLSR
dc.subjectOptimal factorization
dc.subjectConstrained search
dc.titleConstrained numerical optimization of PCR/PLSR predictors
dc.typeJournal article
dc.typePeer reviewed
dc.subject.nsi563
dc.identifier.doihttp://dx.doi.org/10.1016/S0169-7439(02)00159-4


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