Please use this identifier to cite or link to this item:
http://hdl.handle.net/2282/284
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| Title: | Reduced PCR/PLSR models by subspace projections |
| Authors: | Ergon, Rolf |
| Issue Date: | 2006 |
| Publishers version: | http://dx.doi.org/10.1016/j.chemolab.2005.09.008 |
| Abstract: | Latent variables models used in principal component regression (PCR) or partial least squares regression (PLSR) often use a high number of
components, and this makes interpretation of score and loading plots difficult. These plots are essential parts of multivariate modeling, and there is
therefore a need for a reduction of the number of components without loss of prediction power. In this work, it is shown that such reductions of
PCR models with a common number of components for all responses, as well as of PLSR (PLS1 and PLS2) models, may be obtained by
projection of the X modeling objects onto a subspace containing the estimators b Ë
i for the different responses yi . The theoretical results are
substantiated in three real world data set examples, also showing that the presented model reduction method may work quite well also for PCR
models with different numbers of components for different responses, as well as for a set of individual PLSR (PLS1) models. Examples of
interpretational advantages of reduced models in process monitoring applications are included. |
| Keywords: | PCR
PLSR
Model reduction
Subspace projection |
| Publisher: | Elsevier |
| Document type: | Journal article |
| URI: | http://hdl.handle.net/2282/284 |
| Appears in Collections: | Institutt for elektro, IT og kybernetikk
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