Please use this identifier to cite or link to this item:
http://hdl.handle.net/2282/383
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| Title: | Compression into two-component PLS factorizations |
| Authors: | Ergon, Rolf |
| Issue Date: | 2003 |
| Publishers version: | http://dx.doi.org/10.1002/cem.803 |
| Abstract: | Partial least squares regression (PLSR) often requires more than two components also in the case of a
scalar response variable. As shown in papers on orthogonal signal correction (OSC), it is possible to
reduce the number of components, resulting in easier data interpretation. In this paper it is shown
how all scalar response PLSR models can be reduced to two-component models with the same
structure and giving exactly the same estimator as the original model using many components. This
is done by use of a direct and very simple algorithm based on a two-dimensional subspace in the
loading weight space. The resulting model may be transformed into different realizations for
different purposes, e.g. latent variable profile estimation, process monitoring, fault detection, etc.,
as discussed in the paper. |
| Keywords: | PLS factorizations
Parsimonious
Model reduction |
| Publisher: | Wiley |
| Document type: | Journal article |
| URI: | http://hdl.handle.net/2282/383 |
| Appears in Collections: | Institutt for elektro, IT og kybernetikk
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