Results 41 to 50 of about 397,347 (283)
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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Comparison of partial least squares regression, least squares support vector machines, and Gaussian process regression for a near infrared calibration [PDF]
, 2017 This paper investigates the use of least squares support vector machines and Gaussian process regression for multivariate spectroscopic calibration. The performances of these two non-linear regression models are assessed and compared to the traditional ...
Cui, C, Fearn, T
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Deep partial least squares for instrumental variable regression
Applied Stochastic Models in Business and Industry, 2023 AbstractIn this paper, we propose deep partial least squares for the estimation of high‐dimensional nonlinear instrumental variable regression. As a precursor to a flexible deep neural network architecture, our methodology uses partial least squares for dimension reduction and feature selection from the set of instruments and covariates.
Maria Nareklishvili +2 more
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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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A linearization method for partial least squares regression prediction uncertainty [PDF]
, 2014 We study a local linearization approach put forward by Romera to provide an approximate variance for predictions in partial least squares regression.
Fearn, T, Zhang, Y
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Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids [PDF]
, 2014 A combination of systematic density functional theory (DFT) calculations and machine learning techniques has a wide range of potential applications.
Maekawa, Tomoya +3 more
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Distributional aspects in partial least squares regression [PDF]
, 1999 This paper presents some results about the asymptotic behaviour of the estimate of a regression model obtained by Partial Least Squares (PLS) Methods.
Romera, Rosario
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Tide modeling using partial least squares regression [PDF]
Ocean Dynamics, 2020 This research explores the novel use of the partial least squares regression (PLSR) as an alternative model to the conventional least squares (LS) model for modeling tide levels. The modeling is based on twenty tidal constituents: M2, S2, N2, K1, O1, MO3, MK3, MN4, M4, SN4, MS4, 2MN6, M6, 2MS6, S4, SK3, 2MK5, 2SM6, 3MK7, and M8.
Onuwa, Okwuashi +2 more
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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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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)
Cook, Dennis, Zhihua Su
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