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Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample. [PDF]
Netw NeurosciNakua H +9 more
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Fatty acid analysis in microalgal mono- and polycultures using diffuse reflectance infrared Fourier transform spectroscopy coupled with partial least squares analysis. [PDF]
HeliyonNiemi C, Gentili FG.
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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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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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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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Nonlinear partial least squares
Computers & Chemical Engineering, 1997We propose a new nonparametric regression method for high-dimensional data, nonlinear partial least squares (NLPLS), which is motivated by projection-based regression methods, e.g. PLS, projection pursuit regression and feedforward neural networks. The model takes the form of a composition of two functions.
E.C. Malthouse, A.C. Tamhane, R.S.H. Mah
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Partially Generalized Least Squares and Two-Stage Least Squares Estimators
Journal of Econometrics, 1983Abstract A class of partially generalized least squares estimators and a class of partially generalized two-stage least squares estimators in regression models with heteroscedastic errors are proposed. By using these estimators a researcher can attain higher efficiency than that attained by the least squares or the two-stage least squares estimators ...
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Multiview partial least squares
Chemometrics and Intelligent Laboratory Systems, 2017Abstract In practice, multiple distinct features are need to comprehensively analyze complex samples. In machine learning, data set obtained with a feature extractor is referred as a view. Most of data used in practics are collected with various feature extractors.
Yi Mou +5 more
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Partial Least‐Squares Regression
2013This chapter presents the most widely applied and, probably, satisfactory multivariate regression method used nowadays: partial least squares (PLS). Graphical explanations of many concepts are given to complement the more formal mathematical background. Several approaches to solving current problems are suggested.
José Manuel Andrade‐Garda +3 more
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