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Analytical Solution to Partial Least Squares
Information Sciences, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhijiang Lou +3 more
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Boosting Partial Least Squares
Analytical Chemistry, 2005A difficulty when applying partial least squares (PLS) in multivariate calibration is that overfitting may occur. This study proposes a novel approach by combining PLS and boosting. The latter is said to be resistant to overfitting. The proposed method, called boosting PLS (BPLS), combines a set of shrunken PLS models, each with only one PLS component.
Zhang, Menghui +2 more
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Long Range Planning, 2012
Traditional statistical tests are unable to handle a large number of variables. The simplest method to reduce large numbers of variables is the use of add-up scores. But add-up scores do not account for the relative importance of the separate variables, their interactions and differences in units.
Ton J. Cleophas, Aeilko H. Zwinderman
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Traditional statistical tests are unable to handle a large number of variables. The simplest method to reduce large numbers of variables is the use of add-up scores. But add-up scores do not account for the relative importance of the separate variables, their interactions and differences in units.
Ton J. Cleophas, Aeilko H. Zwinderman
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Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression
2012Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table.
Hervé, Abdi, Lynne J, Williams
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A Kernel Partial least square based feature selection method [PDF]
Pattern Recognition, 2018 Maximum relevance and minimum redundancy (mRMR) has been well recognised as one of the best feature selection methods. This paper proposes a Kernel Partial Least Square (KPLS) based mRMR method, aiming for easy computation and improving classification ...
Upasana Talukdar +2 more
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Semi-supervised partial least squares
International Journal of Wavelets, Multiresolution and Information Processing, 2020Traditional supervised dimensionality reduction methods can establish a better model often under the premise of a large number of samples. However, in real-world applications where labeled data are scarce, traditional methods tend to perform poorly because of overfitting.
Xi Jin 0003 +4 more
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Robust Partially-Compressed Least-Squares
Proceedings of the AAAI Conference on Artificial Intelligence, 2017Randomized matrix compression techniques, such as the Johnson-Lindenstrauss transform, have emerged as an effective and practical way for solving large-scale problems efficiently. With a focus on computational efficiency, however, forsaking solutions quality and accuracy becomes the trade-off.
Stephen Becker, Ban Kawas, Marek Petrik
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