Add to ORKGWe discuss several forms of Nonlinear Principal Component Analysis (NLPCA) that have been proposed over the years: Linear PCA with optimal scaling, aspect analysis of correlations, Guttman’s MSA, Logit and Probit PCA of binary data, and Logistic Homogeneity Analysis. They are compared with Multiple Correspondence Analysis (MCA), which we also consider to be a form of NLPCA
Principal component analysis (PCA), also known as proper orthogonal decomposition or Karhunen-Loeve ...
Principal Components are probably the best known and most widely used of all multivariate analysis t...
Linear Principal Component Analysis (LPCA) has been applied in multivariate analysis because of its ...
Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for ma...
Two quite different forms of nonlinear principal component analysis have been proposed in the litera...
This book expounds the principle and related applications of nonlinear principal component analysis ...
Abstract The idea of summarizing the information contained in a large number of variables by a small...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...
In statistical practice multicollinearity of predictor variables is rather the rule than the excepti...
Principal Component Analysis (PCA) is viewed as a descriptive multivariate method for a set of n obs...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
In the social and behavioral sciences, data sets often do not meet the assumptions of traditional an...
Principal Component Analysis (PCA) is one of the most popular techniques in multivariate statistica...
In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a proto...
Principal component analysis (PCA) was first defined in the form that is used nowadays by Pearson (1...
Principal component analysis (PCA), also known as proper orthogonal decomposition or Karhunen-Loeve ...
Principal Components are probably the best known and most widely used of all multivariate analysis t...
Linear Principal Component Analysis (LPCA) has been applied in multivariate analysis because of its ...
Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for ma...
Two quite different forms of nonlinear principal component analysis have been proposed in the litera...
This book expounds the principle and related applications of nonlinear principal component analysis ...
Abstract The idea of summarizing the information contained in a large number of variables by a small...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...
In statistical practice multicollinearity of predictor variables is rather the rule than the excepti...
Principal Component Analysis (PCA) is viewed as a descriptive multivariate method for a set of n obs...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
In the social and behavioral sciences, data sets often do not meet the assumptions of traditional an...
Principal Component Analysis (PCA) is one of the most popular techniques in multivariate statistica...
In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a proto...
Principal component analysis (PCA) was first defined in the form that is used nowadays by Pearson (1...
Principal component analysis (PCA), also known as proper orthogonal decomposition or Karhunen-Loeve ...
Principal Components are probably the best known and most widely used of all multivariate analysis t...
Linear Principal Component Analysis (LPCA) has been applied in multivariate analysis because of its ...