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Tensor Envelope Partial Least-Squares Regression
Technometrics, 2017 Partial least squares (PLS) is a prominent solution for dimension reduction and high-dimensional regressions. Recent prevalence of multidimensional tensor data has led to several tensor versions of the PLS algorithms. However, none offers a population model and interpretation, and statistical properties of the associated parameters remain intractable ...
Zhang, Xin, Li, Lexin
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The pls Package: Principal Component and Partial Least Squares Regression in R [PDF]
Journal of Statistical Software, 2007 The pls package implements principal component regression (PCR) and partial least squares regression (PLSR) in R (R Development Core Team 2006b), and is freely available from the Comprehensive R Archive Network (CRAN), licensed under the GNU General ...
Bjørn-Helge Mevik
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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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On Fuzzy Regression Adapting Partial Least Squares [PDF]
, 2011 Partial Least Squared (PLS) regression is a model linking a dependent variable y to a set of X (numerical or categorical) explanatory variables. It can be obtained as a series of simple and multiple regressions of simple and multiple regressions.
A. BASARAN +2 more
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Partial least-squares regression for soil salinity mapping in Bangladesh
Ecological Indicators, 2023 Estimating the salinity of the soil along the coast of south-western Bangladesh is the focus of this study. Thirteen soil salinity indicators were computed using the Landsat OLI images, and 241 soil salinity samples were gathered from secondary sources ...
Showmitra Kumar Sarkar +3 more
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Compressor map regression modelling based on partial least squares [PDF]
Royal Society Open Science, 2018 In this work, two kinds of partial least squares modelling methods are applied to predict a compressor map: one uses a power function polynomial as the basis function (PLSO), and the other uses a trigonometric function polynomial (PLSN).
Xu Li +6 more
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Partial Least Squares Regression for Binary Responses and Its Associated Biplot Representation
Mathematics, 2022 In this paper, we propose a generalization of Partial Least Squares Regression (PLS-R) for a matrix of several binary responses and a a set of numerical predictors. We call the method Partial Least Squares Binary Logistic Regression (PLS-BLR).
Laura Vicente-Gonzalez +1 more
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APPLICATION OF PARTIAL LEAST SQUARES REGRESSION FOR AUDIO-VISUAL SPEECH PROCESSING AND MODELING [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2015 Subject of Research. The paper deals with the problem of lip region image reconstruction from speech signal by means of Partial Least Squares regression. Such problems arise in connection with development of audio-visual speech processing methods.
A. L. Oleinik
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The Degrees of Freedom of Partial Least Squares Regression [PDF]
, 2010 The derivation of statistical properties for Partial Least Squares regression can be a challenging task. The reason is that the construction of latent components from the predictor variables also depends on the response variable.
Akaike H. +6 more
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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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