Partial Least Squares

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The prediction error in CLS and PLS: the importance of feature selection prior to multivariate calibration (2005, background): “This initial step, in which the dimensionality of the signals is reduced, is often referred to as feature or variable selection, with the most common methods being algorithms that choose a subset of the wavelengths (see e.g. References [26,27,43]).”
Determining the number of components in a factor model from limited noisy data (2008, methodology): “Similarly, in calibration of multi-component systems with non-vanishing correlations between difierent components and with interfering spectral responses, the theoretically optimal number of factors in common algorithms such as partial least squares or principal component regression, is typically eq”
Numerical construction of Green's functions in high dimensional elliptic problems with variable coefficients and analysis of renewable energy data via sparse and separable approximations (2012, methodology): “We begin by citing a relevant result about PLS prediction optimality by Nadler and Coifman [105].”
Finite sample approximation results for principal component analysis: a matrix perturbation approach (2009, background): “1) is an error-invariables linear mixture model, commonly assumed in many different problems and applications, including independent component analysis (ICA) [15], signal processing, and in the analysis of spectroscopic data, where it is known as Beer’s law [25, 27].”