3D QSAR in drug design : theory, methods and applications

By H. Kubinyi. 2000

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Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression (2009, background): “Besides the regression and correlation vectors, other types of vectors occurring in the literature [14,19,20] have the same goal.”
Performance of some variable selection methods when multicollinearity is present (2005, methodology): “PLS-VIP method The VIP score of a predictor, first published in [6], is a summary of the importance for the projections to find h latent variables.”
Frequent substructure-based approaches for classifying chemical compounds (2003, methodology): “This includes techniques based on principle component regression and partial least squares [81], neural networks [5], [27], [57], [85], recursive partitioning [4], [18], [72], phylogenetic-like trees [64], [78], binary QSAR [30], [51], linear discriminant analysis [67], and support vector machines [”
Multivariate Analysis in Metabolomics. (2012, methodology): “A data matrix X, containing N observation row vectors of K variables each, is almost universally common [34, 43, 44], and very few mathematical constraints are placed on the values it holds.”