Partial least squares regression and projection on latent structure regression (PLS Regression)

By Hervé Abdi. 2010

1,399 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Cognitive Neuroscience, Food Science

View Paper: https://www.semanticscholar.org/paper/e6be2250d5b9d4a1671ec2259f97b5aae23be037

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In Analytical Chemistry

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Citations in the corpus

Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables (2011, methodology): “Thus a GPLS regression as an extension of PLS was used since PLS not only retains the original structure but also involves a decomposition into a product of factors and their loadings (regression coefficients), of both the environmental dataset and trait dataset simultaneously (Wold et al. 1984; Abd”
Multiple factor analysis: principal component analysis for multitable and multiblock data sets (2013, methodology): “7–20,145–147 The well-known members of this family—such as STATIS, SUM-PCA, consensus PCA, and multiblock correspondence analysis—all reduce to the PCA of a matrix X in which each X [ k ] has been normalized in a speciﬁc way for each technique.”
Accurate and Precise Prediction of Soil Properties from a Large Mid-Infrared Spectral Library (2019, methodology): “The most common calibration methods for building these models are based on partial least squares regression (PLSR) [12,18,19].”
Is it all about consumer engagement? Explaining continuance intention for utilitarian and hedonic service consumption (2020, methodology): “Given that the partial least squares method maximizes the variance that is explained in the dependent variable (Abdi, 2010; Hair et al., 2011), this technique aligns well with the aim of this research.”
Network-Level Structure-Function Relationships in Human Neocortex (2016, methodology): “PLS is a multivariate statistical technique used to relate two sets of variables to each other (Wold 1966; McIntosh and Lobaugh 2004; Abdi 2010; Krishnan et al. 2011; McIntosh and Mišic ́ 2013).”
Dynamical Adjustment of the Northern Hemisphere Surface Air Temperature Field: Methodology and Application to Observations* (2015, methodology): “When the input variables are column centered and standardized to a variance of unity, as is common in applications of PLSR (Abdi 2010), the weights are crosscorrelation coefficients, and the decomposition of X in (2) yields PLS predictors that are maximally correlated with the predictand. In summary”
Author ' s personal copy Partial Least Squares ( PLS ) methods for neuroimaging : A tutorial and review (2011, positive): “For both PLSC and PLSR, statistical inferences are implemented using computational cross-validation methods (e.g., jackknife, bootstrap).”
Qualinet White Paper on Definitions of Quality of Experience (2013, background): “, External Preference Mapping (Mattila, 2001) or Partial Least Square Regression (Strohmeier, 2011; Abdi, 2010).”
Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India (2020, methodology): “As such, for validation of the conceptual model and for hypotheses-testing, help of Partial Least Square (PLS) – Structural Equation Modelling (SEM) analysis is required to be applied (Wold, Sjostrom, & Eriksson, 2001; Abdi, 2010).”
Enhancing Visible and Near-Infrared Hyperspectral Imaging Prediction of TVB-N Level for Fish Fillet Freshness Evaluation by Filtering Optimal Variables (2017, methodology): “In addition, in order to enhance the application of hyperspectral imaging, chemometric methods such as partial least squares regression (PLSR) and least squares support vector machines (LS-SVM) and others are required (Abdi 2010; Martens et al. 2005; Lorente et al. 2012).”
A Comprehensive Survey on Transfer Learning (2019, methodology): “, partial LSs regression [179]) is performed on the extracted features to generate low-dimension features.”