A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm

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Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry (2014, methodology): “Other authors (Jouan-Rimbaud et al. 1995; Cai et al. 2008; Galvão et al. 2008; Chang 2011) have also explored the feasibility of using more sophisticated methods, such as successive projections algorithm (SPA), uninformative variable elimination (UVE), simulated annealing (SA), artificial neural…”
The successive projections algorithm for interval selection in PLS (2013, background): “More details concerning this final phase are provided elsewhere [35].”
Application of Visible Hyperspectral Imaging for Prediction of Springiness of Fresh Chicken Meat (2015, background): “(2001), aiming to solve the co-linearity problems in spectral data by selecting wavelengths with minimal redundancy (Galvão et al. 2008).”
Machine Learning in Agriculture: A Review (2018, methodology): “Most used SVM algorithms include the support vector regression [67], least squares support vector machine [68], and successive projection algorithm-support vector machine [69].”
Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds (2015, background): “Finally, the uninformative wavelengths, which are irrelevant to classification, are deleted (Galvão et al. 2008).”