@article{Andries2017ImprovedVR, title={Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination.}, author={Jan P M Andries and Yvan Vander Heyden and Lutgarde M. C. Buydens}, journal={Analytica chimica acta}, year={2017}, volume={982}, pages={37-47} }

- Published 2017 in Analytica chimica acta
DOI:10.1016/j.aca.2017.06.001

The calibration performance of Partial Least Squares regression (PLS) can be improved by eliminating uninformative variables. For PLS, many variable elimination methods have been developed. One is the Uninformative-Variable Elimination for PLS (UVE-PLS). However, the number of variables retained by UVE-PLS is usually still large. In UVE-PLS, variable elimination is repeated as long as the root mean squared error of cross validation (RMSECV) is decreasing. The set of variables in this first… CONTINUE READING

### Presentations referencing similar topics