Results 11 to 20 of about 5,933,507 (244)

Compressor map regression modelling based on partial least squares [PDF]

open access: yesRoyal 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
doaj   +1 more source

Partial Least Squares Regression for Binary Responses and Its Associated Biplot Representation

open access: yesMathematics, 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
doaj   +1 more source

Orthogonal least squares regression with tunable kernels [PDF]

open access: yes, 2005
A novel technique is proposed to construct sparse regression models based on the orthogonal least squares method with tunable kernels. The proposed technique tunes the centre vector and diagonal covariance matrix of individual regressor by incrementally ...
Wang, X.X., Chen, S., Brown, D.J.
core   +2 more sources

Marginal Screening for Partial Least Squares Regression

open access: yesIEEE Access, 2017
Partial least squares (PLS) regression is a versatile modeling approach for high-dimensional data analysis. Recently, PLS-based variable selection has attracted great attention due to high-throughput data reduction and modeling interpretability.
Naifei Zhao, Qingsong Xu, Hong Wang
doaj   +1 more source

PARTIAL LEAST SQUARES REGRESSION $PLS$ ON INTERVAL DATA

open access: yesRevista 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
doaj   +1 more source

Identification of Browning in Human Adipocytes by Partial Least Squares Regression (PLSR), Infrared Spectral Biomarkers, and Partial Least Squares Discriminant Analysis (PLS-DA) Using FTIR Spectroscopy

open access: yesPhotonics, 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
doaj   +1 more source

Partial least squares regression in the social sciences [PDF]

open access: yesTutorials 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
doaj   +2 more sources

APPLICATION OF PARTIAL LEAST SQUARES REGRESSION FOR AUDIO-VISUAL SPEECH PROCESSING AND MODELING [PDF]

open access: yesНаучно-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
doaj   +1 more source

Partial least square regression applied to the QTLMAS 2010 dataset [PDF]

open access: yes, 2010
Detection of genomic regions affecting traits is a goal in many genetic studies. Studies applying distinct methods for detection of these regions, called quantitative trait loci (QTL), have been described, ranging from single marker regression [1] to ...
Calus, M.P.L.   +3 more
core   +1 more source

Filter-Based Factor Selection Methods in Partial Least Squares Regression

open access: yesIEEE 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
doaj   +1 more source

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