Results 231 to 240 of about 398,426 (282)

Partial least-squares regression: a tutorial

Analytica Chimica Acta, 1986
Abstract A tutorial on the partial least-squares (PLS) regression method is provided. Weak points in some other regression methods are outlined and PLS is developed as a remedy for those weaknesses. An algorithm for a predictive PLS and some practical hints for its use are given.
Paul Geladi, Bruce R. Kowalski
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Robust methods for partial least squares regression

Journal of Chemometrics, 2003
AbstractPartial least squares regression (PLSR) is a linear regression technique developed to deal with high‐dimensional regressors and one or several response variables. In this paper we introduce robustified versions of the SIMPLS algorithm, this being the leading PLSR algorithm because of its speed and efficiency.
M. Hubert, K. Vanden Branden
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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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Domain-Invariant Partial-Least-Squares Regression

Analytical Chemistry, 2018
Multivariate calibration models often fail to extrapolate beyond the calibration samples because of changes associated with the instrumental response, environmental condition, or sample matrix. Most of the current methods used to adapt a source calibration model to a target domain exclusively apply to calibration transfer between similar analytical ...
Ramin Nikzad-Langerodi, Werner Zellinger, Edwin Lughofer, Susanne Saminger-Platz   +3 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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Domain adaptive partial least squares regression

Chemometrics and Intelligent Laboratory Systems, 2020
Abstract In practical applications, the problem of training- and test-samples from different distributions is often encountered, such as instruments or external environmental factors change when measuring the data. Therefore, a multivariate calibration model established, based on the training set needs to be adaptive to meet the requirements of test ...
Guangzao Huang, Xiaojing Chen, Limin Li, Xi Chen, Leiming Yuan, Wen Shi   +5 more
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Kernel Partial Least-Squares Regression

The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
A 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 ...
null Bai Yifeng, null Xiao Jian, null Yu Long   +2 more
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Spectral Partial Least Squares Regression

IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 2010
Linear Graph Embedding (LGE) is the linearization of graph embedding, and has been applied in many domains successfully. However, the high computational cost restricts these algorithms to be applied to large scale high dimensional data sets. One major limitation of such algorithms is that the generalized eigenvalue problem is computationally expensive ...
Jiangfeng Chen, Baozong Yuan
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Microwave characterization using partial least square regression

2016 IEEE Conference on Electromagnetic Field Computation (CEFC), 2016
Inverse problems for determination of dielectric materials properties (complex permittivity) are usually solved by iterative methods using numerically based forward model. These methods are computationally expensive. In this paper, we propose a fast inversion model based on partial least square regression (PLSR).
Sadou, Hakim, Hacib, Tarik, Le Bihan, Yann, Acikgoz, Hulusi, Meyer, Olivier   +4 more
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