Please use this identifier to cite or link to this item:
http://hdl.handle.net/2282/286
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| Title: | Constrained numerical optimization of PCR/PLSR predictors |
| Authors: | Ergon, Rolf |
| Issue Date: | 2003 |
| Publishers version: | http://dx.doi.org/10.1016/S0169-7439(02)00159-4 |
| 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. |
| Keywords: | PCR/PLSR
Optimal factorization
Constrained search |
| Publisher: | Elsevier |
| Document type: | Journal article |
| URI: | http://hdl.handle.net/2282/286 |
| Appears in Collections: | Institutt for elektro, IT og kybernetikk
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