Results 291 to 300 of about 880,930 (320)

Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression

2012
Partial 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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Partial Least Squares

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, Evan L. Russell, Richard D. Braatz   +2 more
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Nonlinear partial least squares

Computers & Chemical Engineering, 1997
We 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, 1983
Abstract 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, 2017
Abstract 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, Long Zhou, Xinge You, Yaling Lu, Weizhen Chen, Xu Zhao   +5 more
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Partial Least‐Squares Regression

2013
This 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, Alatzne Carlosena‐Zubieta, Ricard Boqué‐Martí, Joan Ferré‐Baldrich   +3 more
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Implementing partial least squares

Statistics and Computing, 1995
Partial least squares (PLS) regression has been proposed as an alternative regression technique to more traditional approaches such as principal components regression and ridge regression. A number of algorithms have appeared in the literature which have been shown to be equivalent.
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Partial Least Squares Path Modeling

2017
Structural equation modeling (SEM) is a family of statistical techniques that has become very popular in marketing. Its ability to model latent variables, to take various forms of measurement error into account, and to test entire theories makes it useful for a plethora of research questions.
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Optimized sample-weighted partial least squares

Talanta, 2007
In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored.
Lu, Xu, Jian-Hui, Jiang, Wei-Qi, Lin, Yan-Ping, Zhou, Hai-Long, Wu, Guo-Li, Shen, Ru-Qin, Yu   +6 more
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Vehicle Detection Using Partial Least Squares

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
Detecting vehicles in aerial images has a wide range of applications, from urban planning to visual surveillance. We describe a vehicle detector that improves upon previous approaches by incorporating a very large and rich set of image descriptors. A new feature set called Color Probability Maps is used to capture the color statistics of vehicles and ...
Aniruddha, Kembhavi, David, Harwood, Larry S, Davis   +2 more
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