Orthogonal Partial Least Squares Regression (OPLSR)
“Feature Selection Using Distribution of Orthogonal PLS Regression Vectors in Spectral Data”
This paper introduces a useful combined approach of applying orthogonal signal correction (OSC) and permutation tests to PLS for the purpose of feature selection.
- There are three methods for feauture selection: OPLSR, FDR, and Lasso.
- Algorithms used in experiments can be implemented by running
$ matlab test_FDR_PCR.m
$ matlab test_linear.m
$ matlab testPCRselection_Data.m
- The matters related to the creation and experimentation of simulation data are in ./Simulation.
- All the datasets, Metabolomics and NIR spectra, are in ./data.
- To produce output, each methods require some useful functions for calculation, call and etc. The functions are in ./utils.