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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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2000
Partial Least Squares (PLS), also known as Projection to Latent Structures, is a dimensionality reduction technique for maximizing the covariance between the predictor (independent) matrix X and the predicted (dependent) matrix Y for each component of the reduced space [61, 235].
Leo H. Chiang +2 more
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Partial Least Squares (PLS), also known as Projection to Latent Structures, is a dimensionality reduction technique for maximizing the covariance between the predictor (independent) matrix X and the predicted (dependent) matrix Y for each component of the reduced space [61, 235].
Leo H. Chiang +2 more
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Approximate kernel partial least squares
Annals of Mathematics and Artificial Intelligence, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiling Liu, Shuisheng Zhou
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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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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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Shrinkage Structure of Partial Least Squares
Scandinavian Journal of Statistics, 2000Partial least squares regression (PLS) is one method to estimate parameters in a linear model when predictor variables are nearly collinear. One way to characterize PLS is in terms of the scaling (shrinkage or expansion) along each eigenvector of the predictor correlation matrix. This characterization is useful in providing a link between PLS and other
Lingjærde, Ole C., Christophersen, Nils
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Kernel Partial Least-Squares Regression
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006A couple of regularized least squares regression models in a feature space are extended by the kernel partial least squares (KPLS) regression model in this paper. PLS is a method based on the projection of input (explanatory) variables to the latent variables (components), and has been developed and established as one of the multivariate statistical ...
Bai Yifeng, Xiao Jian, Yu Long
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