dc.contributor.author | Ergon, Rolf | |
dc.date.accessioned | 2007-02-08T09:44:55Z | |
dc.date.accessioned | 2017-04-19T12:49:52Z | |
dc.date.available | 2007-02-08T09:44:55Z | |
dc.date.available | 2017-04-19T12:49:52Z | |
dc.date.issued | 2003 | |
dc.identifier.citation | Chemometrics and intelligent laboratory systems 65 (2003), No. 2, p. 293-303 | |
dc.identifier.issn | 0169-7439 | |
dc.identifier.uri | http://hdl.handle.net/11250/2438417 | |
dc.description.abstract | Assuming 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.extent | 704235 bytes | |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Elsevier | |
dc.subject | PCR/PLSR | |
dc.subject | Optimal factorization | |
dc.subject | Constrained search | |
dc.title | Constrained numerical optimization of PCR/PLSR predictors | |
dc.type | Journal article | |
dc.type | Peer reviewed | |
dc.subject.nsi | 563 | |
dc.identifier.doi | http://dx.doi.org/10.1016/S0169-7439(02)00159-4 | |