Variable and subset selection in PLS regression

By Agnar Höskuldsson. 2001

268 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/b3d7bb1de07a6022af466a84162c27c3322a1ec9

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In Analytical Chemistry

In Control and Systems Engineering

In Industrial and Manufacturing Engineering

Citations in the corpus

Enhancing Visible and Near-Infrared Hyperspectral Imaging Prediction of TVB-N Level for Fish Fillet Freshness Evaluation by Filtering Optimal Variables (2017, background): “…fusion can be beneficial to improve the reliability and accuracy of the prediction models, which was also confirmed and reported in another work by Huang et al. (2014), who integrated the information of NIR spectroscopy, computer vision, and electronic nose techniques for measurement of TVB-N in…”
Variable selection in regression—a tutorial (2010, background): “It is better to choose windows of wavelengths [1].”
Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression (2009, background): “Hoskuldsson and Reinikainen [12,13,21] have proposed informative vectors and strategies for variable selection.”
A twist to partial least squares regression (2005, methodology): “The left column of Figure 4 confirms Höskuldsson’s [5] observation that not much of y is explained by the first PLS components.”
A review of variable selection methods in Partial Least Squares Regression (2012, background): “For instance, in such cases there is a problem with the asymptotic consistency of the PLS estimators for univariate responses [2], and from a prediction perspective the large number of irrelevant variables may yield large variation on test set prediction [8].”
A method for screening active effects in supersaturated designs (2007, background): “Recent work focusing on this topic includes, e.g. Helland (1990), Garthwaite (1994), Butler and Denham (2000), Höskuldsson (2001) and Bastien et al. (2005).”
Multivariate Analysis in Metabolomics. (2012, methodology): “Unfortunately, this exacerbates the aforementioned collinearity problem and increases the likelihood of finding spurious correlations in data, leading to a greater chance of inferring incorrect biological conclusions [46, 51].”