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Group‐wise partial least square regression
Journal of Chemometrics, 2017 AbstractThis paper introduces the group‐wise partial least squares (GPLS) regression. GPLS is a new sparse PLS technique where the sparsity structure is defined in terms of groups of correlated variables, similarly to what is done in the related group‐wise principal component analysis. These groups are found in correlation maps derived from the data to
José Camacho, Edoardo Saccenti
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The Degrees of Freedom of Partial Least Squares Regression [PDF]
Journal of the American Statistical Association, 2011 Preprint: Weierstraß-Institut für Angewandte Analysis und Stochastik, vol ...
Sugiyama, Masashi, Krämer, Nicole
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Filter-Based Factor Selection Methods in Partial Least Squares Regression
IEEE Access, 2019 Factor discovery of high-dimensional data is a crucial problem and extremely challenging from a scientific viewpoint with enormous applications in research studies. In this study, the main focus is to introduce the improved subset factor selection method
Tahir Mehmood +2 more
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Fault identification for chiller sensor based on partial least square method [PDF]
E3S Web of Conferences, 2021 Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component ...
Wu Bang +4 more
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Scaled Predictor Envelopes and Partial Least-Squares Regression [PDF]
Technometrics, 2016 Partial least squares (PLS) is a widely used method for prediction in applied statistics, especially in chemometrics applications. However, PLS is not invariant or equivariant under scale transformations of the predictors, which tends to limit its scope to regressions in which the predictors are measured in the same or similar units. Cook et al. (2013)
R. Dennis Cook, Zhihua Su
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Robust Nonlinear Partial Least Squares Regression Using the BACON Algorithm
Journal of Applied Mathematics, 2018 Partial least squares regression (PLS regression) is used as an alternative for ordinary least squares regression in the presence of multicollinearity. This occurrence is common in chemical engineering problems.
Abdelmounaim Kerkri +2 more
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On function-on-function regression: partial least squares approach [PDF]
Environmental and Ecological Statistics, 2020 Functional data analysis tools, such as function-on-function regression models, have received considerable attention in various scientific fields because of their observed high-dimensional and complex data structures. Several statistical procedures, including least squares, maximum likelihood, and maximum penalized likelihood, have been proposed to ...
Ufuk Beyaztas, Han Lin Shang
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Partial Least Squares Regression for Binary Data
MathematicsClassical Partial Least Squares Regression (PLSR) models were developed primarily for continuous data, allowing dimensionality reduction while preserving relationships between predictors and responses. However, their application to binary data is limited.
Laura Vicente-Gonzalez +2 more
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Fast Multiway Partial Least Squares Regression
IEEE Transactions on Biomedical Engineering, 2019 Multiway array decomposition has been successful in providing a better understanding of the structure underlying data and in discovering potentially hidden feature dependences serving high-performance decoder applications. However, the computational cost of multiway algorithms can become prohibitive, especially when considering large datasets ...
Camarrone, Flavio, Van Hulle, Marc M.
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گۆڤارا زانستێن مرۆڤایەتی یا زانكۆیا زاخۆ, 2022 This paper was dealing with variables for MAS Cement Factory where evince many problems , more than one variable dependent and presence the problem of multicollinearity and so presence the correlation between the predictive variables and the dependent ...
Sherin mohyaldeen, Mohammed Alhassawy
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