Partial Least Squares (PLS) Regression.

By Hervé Abdi. 2003

298 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cbddc67a292c443373dfdb2f41ed4800e799f1f5

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Citations in the corpus

A Comparison of Partial Least Squares (PLS) and Ordinary Least Squares (OLS) regressions in predicting of couples mental health based on their communicational patterns (2010, background): “The goal of PLS is to predict y from x and to describe the common structure underlying the two variables (Abdi , 2003).”
Data-driven Soft Sensors in the process industry (2009, methodology): “, 2001; Abdi, 2003). These two approaches are the most popular ones to deal with data co-linearity in the process industry. Examples of applications where PCA is used are: Lin et al. (2007); Amazouz and Pantea (2006); Wang and Cui (2005); Zhao and Chai (2004) and for the PLS Marjanovic et al. (2006)”
Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging (2019, methodology): “The PLS method has been explained in detail in numerous literature (e.g. Abdi 2003), and here only a summary of the PLS method is presented.”
Prediction of the Daily Mean PM10 Concentrations Using Linear Models (2008, methodology): “PLSR is based on linear transition from a large number of original descriptors to a new variable space based on small number of orthogonal factors (latent variables - LV) [ 16 ]”
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance (2013, methodology): “Partial Least Squares (PLS) iteratively modifies the loadings of the explanatory 360 variables on a PCA in order to maximise the fit of the PCA regression onto the response variable y (Abdi 2003).”