Results 31 to 40 of about 614,693 (221)
Comparison of variable selection methods in partial least squares regression
Journal of Chemometrics, 2020 Through the remarkable progress in technology, it is getting easier and easier to generate vast amounts of variables from a given sample. The selection of variables is imperative for data reduction and for understanding the modeled relationship.
T. Mehmood, S. Sæbø, K. H. Liland
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The Degrees of Freedom of Partial Least Squares Regression [PDF]
, 2010 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.
Akaike H. +6 more
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Locality-Preserving Partial Least Squares Regression [PDF]
, 2022 AbstractThis chapter proposes another nonlinear PLS method, named as locality-preserving partial least squares (LPPLS), which embeds the nonlinear degenerative and structure-preserving properties of LPP into the PLS model. The core of LPPLS is to replace the role of PCA in PLS with LPP. When extracting the principal components of $$\boldsymbol{t}_i$$
Jing Wang, Jinglin Zhou, Xiaolu Chen
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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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Sparse partial least squares regression for simultaneous dimension reduction and variable selection
Journal of The Royal Statistical Society Series B-statistical Methodology, 2010 Summary. Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional
Hyonho Chun, S. Keleş
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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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Journal of Neural Engineering, 2020 Objective. Due to low spatial resolution and poor signal-to-noise ratio of electroencephalogram (EEG), high accuracy classifications still suffer from lots of obstacles in the context of motor imagery (MI)-based brain-machine interface (BMI) systems ...
Yaqi Chu +6 more
semanticscholar +1 more source
Convergence rates of Kernel Conjugate Gradient for random design regression [PDF]
, 2016 We prove statistical rates of convergence for kernel-based least squares regression from i.i.d. data using a conjugate gradient algorithm, where regularization against overfitting is obtained by early stopping.
Blanchard, Gilles, Krämer, Nicole
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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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The use of partial least squares path modeling in causal inference for archival financial accounting research [PDF]
, 2014 In financial accounting research, multivariate regression is almost exclusively the dominant statistical method. By contrast, Partial Least Squares path modeling is a under-utilized statistical method.
Ali, Mohammad Bilal +2 more
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