Two quite different forms of nonlinear principal component analysis have been proposed in the literature. The first one is associated with the names of Guttman, Burt, Hayashi, Benzécri, McDonald, De Leeuw, Hill, Nishisato. We call it multiple correspondence analysis. The second form has been discussed by Kruskal, Shepard, Roskam, Takane, Young, De Leeuw, Winsberg, Ramsay. We call it nonmetric principal component analysis. The two forms have been related and combined, both geometrically and computationally, by Albert Gifi. In this paper we discuss the relationships in more detail, and propose an alternative algorithm for nonlinear principal component analysis which combines features of both previous approaches
Linear Principal Component Analysis (LPCA) has been applied in multivariate analysis because of its ...
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use...
Nonlinear Principal Components Analysis (PCA) addresses the nonlinearity problem by relaxing the lin...
Two quite different forms of nonlinear principal component analysis have been proposed in the litera...
We discuss several forms of Nonlinear Principal Component Analysis (NLPCA) that have been proposed o...
Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for ma...
Abstract The idea of summarizing the information contained in a large number of variables by a small...
This book expounds the principle and related applications of nonlinear principal component analysis ...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...
This paper summarizes a new concept to determine principal curves for nonlinear principal component ...
Principal Component Analysis (PCA) is viewed as a descriptive multivariate method for a set of n obs...
Principal component analysis (PCA), also known as proper orthogonal decomposition or Karhunen-Loeve ...
This paper suggests a method to generalise principal component analysis by transforming in adaptive ...
We propose a novel algorithm based on Principal Component Analysis (PCA). First, we present an inter...
In statistical practice multicollinearity of predictor variables is rather the rule than the excepti...
Linear Principal Component Analysis (LPCA) has been applied in multivariate analysis because of its ...
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use...
Nonlinear Principal Components Analysis (PCA) addresses the nonlinearity problem by relaxing the lin...
Two quite different forms of nonlinear principal component analysis have been proposed in the litera...
We discuss several forms of Nonlinear Principal Component Analysis (NLPCA) that have been proposed o...
Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for ma...
Abstract The idea of summarizing the information contained in a large number of variables by a small...
This book expounds the principle and related applications of nonlinear principal component analysis ...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...
This paper summarizes a new concept to determine principal curves for nonlinear principal component ...
Principal Component Analysis (PCA) is viewed as a descriptive multivariate method for a set of n obs...
Principal component analysis (PCA), also known as proper orthogonal decomposition or Karhunen-Loeve ...
This paper suggests a method to generalise principal component analysis by transforming in adaptive ...
We propose a novel algorithm based on Principal Component Analysis (PCA). First, we present an inter...
In statistical practice multicollinearity of predictor variables is rather the rule than the excepti...
Linear Principal Component Analysis (LPCA) has been applied in multivariate analysis because of its ...
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use...
Nonlinear Principal Components Analysis (PCA) addresses the nonlinearity problem by relaxing the lin...