Marginal Screening for Partial Least Squares Regression
IEEE 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
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Quartz-Enhanced Photoacoustic Spectroscopy Assisted by Partial Least-Squares Regression for Multi-Gas Measurements [PDF]
Sensors, 2023 We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photoacoustic (PA) spectra of mixtures of water (H2O), ammonia (NH3), and methane (CH4) were measured in the mid-infrared (MIR) wavelength range using a ...
Andreas N. Rasmussen +4 more
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Downscaling GRACE total water storage change using partial least squares regression [PDF]
Scientific Data, 2021 Measurement(s) Gravity Technology Type(s) gravity field theory • computational modeling technique Factor Type(s) geographic location • temporal interval Sample Characteristic - Environment water body Sample Characteristic - Location global Machine ...
Bramha Dutt Vishwakarma +2 more
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Partial Least Squares Regression for Binary Responses and Its Associated Biplot Representation [PDF]
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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Lavender hydrosol analysis using UV spectroscopy data and partial least squares regression [PDF]
MethodsXThe aim of our work was to estimate the composition of hydrosol produced as a byproduct of lavender steam distillation using UV–Vis spectrophotometry in the 200–600 nm wavelength range through a machine learning algorithm.
Sára Preiner +3 more
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The Degrees of Freedom of Partial Least Squares Regression [PDF]
, 2011 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.
Nicole Krämer, Masashi Sugiyama
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Partial Least Squares Regression Performs Well in MRI-Based Individualized Estimations [PDF]
Frontiers in Neuroscience, 2019 Estimation of individuals’ cognitive, behavioral and demographic (CBD) variables based on MRI has attracted much research interest in the past decade, and effective machine learning techniques are of great importance for these estimations.
Chen Chen, Xuyu Cao, Lixia Tian
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Development of partial least squares regression with discriminant analysis for software bug prediction [PDF]
HeliyonMany prediction models and approaches have been introduced during the past decades that try to forecast bugged code elements based on static source code metrics, change and history metrics, or both.
Róbert Rajkó +3 more
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Partial Least Squares Regression-Based Robust Forward Control of the Tableting Process [PDF]
Pharmaceutics, 2020 In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR) spectra and physical attributes of the granules to be compressed.
Yusuke Hattori +2 more
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Gene Function Prediction from Functional Association Networks Using Kernel Partial Least Squares Regression. [PDF]
PLoS ONE, 2015 With the growing availability of large-scale biological datasets, automated methods of extracting functionally meaningful information from this data are becoming increasingly important.
Sonja Lehtinen +4 more
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