A review of variable selection methods in Partial Least Squares Regression

By Tahir Mehmood, Kristian Hovde Liland, Lars-Gustav Snipen, Solve Sæbø. 2012

1,473 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/502c23dc7b6fa02cee43da7cae8459739d0c73d5

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In Analytical Chemistry

In Biomedical Engineering

In Statistics and Probability

Citations in the corpus

The successive projections algorithm for interval selection in PLS (2013, background): “Although this topic has been the subject of extensive investigations, it is still a matter of much research in the literature [6,7].”
Estimation of soil properties at the field scale from satellite data: a comparison between spatial and non‐spatial techniques (2014, background): “Although many studies have obtained strong correlations between SOM and reflectance in the visible (VIS) and near infra-red (NIR) regions, the best correlations between SOM and wavelengths always occurred in the SWIR region (Melendez-Pastor et al., 2008).”
Improved prediction of ‘Kent’ mango firmness during ripening by near-infrared spectroscopy supported by interval partial least square regression (2020, background): “In that case, the identification of sub-wavelengths in the spectrum can help in identifying the main predictor variables required to explain the variance in the response variables [13].”
Rapid Quantification Analysis and Visualization of Escherichia coli Loads in Grass Carp Fish Flesh by Hyperspectral Imaging Method (2015, methodology): “Somefrequentlyusedvariable selection methods such as genetic algorithm (Arakawa et al. 2011), PLS regression coefficients and stepwise regression (Mehmood et al. 2012), successive projections algorithm (Ghasemi-Varnamkhasti et al. 2012) and uninformative variable elimination (Balabin and Smirnov…”
The sustainable development oxymoron: quantifying and modelling the incompatibility of sustainable development goals (2016, neutral): “Specifically, we used the Variable Elimination Algorithm (Mehmood et al. 2012), a supervised feature selection machine learning method based on partial least squares regression.”