Results 21 to 30 of about 91,920 (292)
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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Envelopes and Partial Least Squares Regression
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2013 SummaryWe build connections between envelopes, a recently proposed context for efficient estimation in multivariate statistics, and multivariate partial least squares (PLS) regression. In particular, we establish an envelope as the nucleus of both univariate and multivariate PLS, which opens the door to pursuing the same goals as PLS but using ...
Cook, R. D., Helland, I. S., Su, Z.
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Partial least squares regression in the social sciences [PDF]
Tutorials in Quantitative Methods for Psychology, 2015 Partial least square regression (PLSR) is a statistical modeling technique that extracts latent factors to explain both predictor and response variation.
Megan L. Sawatsky +2 more
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PARTIAL LEAST SQUARES REGRESSION $PLS$ ON INTERVAL DATA
Revista de la Facultad de Ciencias, 2016 Uncertainty in the data can be considered as a numerical interval in which a variable can assume its possible values, this has been known as interval data. In this paper the $PLS$ regression methodology is extended to the case where explanatory, response
Carlos Alberto Gaviria-Peña +2 more
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Photonics, 2022 We aimed to identify the browning of white adipocytes using partial least squares regression (PLSR), infrared spectral biomarkers, and partial least squares discriminant analysis (PLS-DA) with FTIR spectroscopy instead of molecular biology.
Dong-Hyun Shon +4 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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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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