Please use this identifier to cite or link to this item:
http://hdl.handle.net/2282/384
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| Title: | PCR/PLSR optimization based on noise covariance estimation and Kalman filtering theory |
| Authors: | Ergon, Rolf
Esbensen, Kim H. |
| Issue Date: | 2002 |
| Publishers version: | http://dx.doi.org/10.1002/cem.732 |
| Abstract: | The theoretical connection between principal component regression (PCR) and partial least squares
regression (PLSR) on one hand and Kalman filtering (KF) on the other is known from earlier work. In
the present paper we investigate the possibilities to use latent variables modeling and KF theory as
means for optimization of ordinary PLSR and PCR predictors, based on the prerequisite of prior X
noise covariance estimates facilitated e.g. by more X than y observations. The result is a new PLSR
optimization method, while the PCR optimization turns out to be identical with an earlier known
method. A simulation example and two real-world data examples supporting the theoretical
development are presented. The treatment is limited to cases with only one response variable,
although an extension to multiresponse cases is also possible. |
| Keywords: | PLSR/PCR
Optimization
Kalman filtering
Covariance estimation |
| Publisher: | Wiley |
| Document type: | Journal article |
| URI: | http://hdl.handle.net/2282/384 |
| Appears in Collections: | Institutt for elektro, IT og kybernetikk
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