Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination.

@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}
}
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