Multivariate Prediction with Nonlinear Principal Components Analysis: Theory
- JOHN GOWER
- JÖRG BLASIUS
DOIAbstract
biplot, large scale data analysis, nonlinear principal components analysis, prediction,
biplot, large scale data analysis, nonlinear principal components analysis, prediction,
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
International audienceWe introduce an algorithm which, in the context of nonlinear regression on vec...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...
Gower and Blasius (Quality and Quantity, 39, 2005) proposed the notion of multivariate predictabilit...
Gower and Blasius (Quality and Quantity, 39, 2005) proposed the notion of multivariate predictabilit...
Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for ma...
multiple regression, optimal scaling, optimal scoring, statistical learning, data mining, boosting, ...
In statistical practice multicollinearity of predictor variables is rather the rule than the excepti...
In this study, Kernel Principal Component Analysis (KPCA) is applied as feature selection in a high-...
We discuss several forms of Nonlinear Principal Component Analysis (NLPCA) that have been proposed o...
Abstract The idea of summarizing the information contained in a large number of variables by a small...
<p>Global, multivariate correlation analysis. On the Biplot each body location is represented by pol...
This book expounds the principle and related applications of nonlinear principal component analysis ...
Principal Component Analysis (PCA) is one of the most popular techniques in multivariate statistica...
In statistical practice multicollinearity of predictor variables is rather the rule than the excepti...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
International audienceWe introduce an algorithm which, in the context of nonlinear regression on vec...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...
Gower and Blasius (Quality and Quantity, 39, 2005) proposed the notion of multivariate predictabilit...
Gower and Blasius (Quality and Quantity, 39, 2005) proposed the notion of multivariate predictabilit...
Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for ma...
multiple regression, optimal scaling, optimal scoring, statistical learning, data mining, boosting, ...
In statistical practice multicollinearity of predictor variables is rather the rule than the excepti...
In this study, Kernel Principal Component Analysis (KPCA) is applied as feature selection in a high-...
We discuss several forms of Nonlinear Principal Component Analysis (NLPCA) that have been proposed o...
Abstract The idea of summarizing the information contained in a large number of variables by a small...
<p>Global, multivariate correlation analysis. On the Biplot each body location is represented by pol...
This book expounds the principle and related applications of nonlinear principal component analysis ...
Principal Component Analysis (PCA) is one of the most popular techniques in multivariate statistica...
In statistical practice multicollinearity of predictor variables is rather the rule than the excepti...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
International audienceWe introduce an algorithm which, in the context of nonlinear regression on vec...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...
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