Results 31 to 40 of about 91,920 (292)
Fault identification for chiller sensor based on partial least square method [PDF]
E3S Web of Conferences, 2021 Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component ...
Wu Bang +4 more
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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)
R. Dennis Cook, Zhihua Su
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On partial least‐squares estimation in scalar‐on‐function regression models [PDF]
Journal of Chemometrics, 2022 Abstract Scalar‐on‐function regression, where the response is scalar valued and the predictor consists of random functions, is one of the most important tools for exploring the functional relationship between a scalar response and functional predictor(s).
Semanur Saricam +3 more
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A note on between-group PCA [PDF]
, 2004 In the context of binary classification with continuous predictors, we proove two properties concerning the connections between Partial Least Squares (PLS) dimension reduction and between-group PCA, and between linear discriminant analysis and between ...
Anne-laure Boulesteix +1 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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Regularized estimation of large-scale gene association networks using graphical Gaussian models [PDF]
, 2009 Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial ...
Schäfer, Juliane +10 more
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On function-on-function regression: partial least squares approach [PDF]
Environmental and Ecological Statistics, 2020 Functional data analysis tools, such as function-on-function regression models, have received considerable attention in various scientific fields because of their observed high-dimensional and complex data structures. Several statistical procedures, including least squares, maximum likelihood, and maximum penalized likelihood, have been proposed to ...
Ufuk Beyaztas, Han Lin Shang
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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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Partial least squares for classification: a new point of view [PDF]
, 2022 Nowadays data are everywhere and it becomes increasingly important to collect and analyze them in the correct way in order to obtain useful information, since a broad number of fields on a scientific and industrial level need data analysis to solve a ...
De Nardi, Martino
core
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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