Results 251 to 260 of about 112,882 (301)

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 median of squares regression

Journal of Chemometrics, 2022
Abstract In modern data analysis, there is an increasing availability of datasets with numerous variables. Linear models that deal with abundant predictor variables often have poor performance because they tend to produce large variances. As well known, partial least squares (PLS) regression standouts because it is serviceable even if
Zhonghao Xie, Xi'an Feng, Limin Li, Xiaojing Chen   +3 more
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A Reformulation of the Partial Least Squares Regression Algorithm

SIAM Journal on Scientific Computing, 1994
Let \(X = (x_ 1,\dots,x_ k)\), where \(x_ 1,\dots,x_ k\) are \(n\)- dimensional vectors (independent variables). Also available is an associated \(n\)-dimensional vector \(y\) (dependent variable). One of the main aims of linear regression is to predict the values of the dependent variable using a linear combination of the independent variables ...
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The objective function of partial least squares regression

Journal of Chemometrics, 1998
A simple objective function in terms of undeflated X is derived for the latent variables of multivariate PLS regression. The objective function fits into the basic framework put forward by Burnham et al. (J. Chemometrics, 10, 31–45 (1996)). We show that PLS and SIMPLS differ in the constraint put on the length of the X-weight vector.
ter Braak, C.J.F., de Jong, S.
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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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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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Sparse Kernel Partial Least Squares Regression

2003
Partial Least Squares Regression (PLS) and its kernel version (KPLS) have become competitive regression approaches. KPLS performs as well as or better than support vector regression (SVR) for moderately-sized problems with the advantages of simple implementation, less training cost, and easier tuning of parameters.
Michinari Momma, Kristin P. Bennett
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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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A twist to partial least squares regression

Journal of Chemometrics, 2005
AbstractA modification of the PLS1 algorithm is presented. Stepwise optimization over a set of candidate loading weights obtained by taking powers of the y–X correlations and X standard deviations generalizes the classical PLS1 based on y–X covariances and hence adds flexibility to the modelling.
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