CARME 2011 - History of Nonlinear Principal Component Analysis - Jan de Leeuw
Talk given by Jan de Leeuw on the occasion of the CARME 2011 organized by the Applied Mathematic Departement. Abstract: Multiple Correspondence Analysis (MCA) is discussed as a form of Nonlinear Principal Component Analysis (NLPCA). It is compared with other forms of NLPCA that have been proposed over the years: Shepard-Kruskal-Breiman-Friedman-Gi PCA with optimal scaling, aspect analysis of correlations, Guttman's MSA, Logit/Probit PCA of binary data, and Logistic Homogeneity Analysis.
Talk given by Jan de Leeuw on the occasion of the CARME 2011 organized by the Applied Mathematic Departement. Abstract: Multiple Correspondence Analysis (MCA) is discussed as a form of Nonlinear Principal Component Analysis (NLPCA). It is compared with other forms of NLPCA that have been proposed over the years: Shepard-Kruskal-Breiman-Friedman-Gi PCA with optimal scaling, aspect analysis of correlations, Guttman's MSA, Logit/Probit PCA of binary data, and Logistic Homogeneity Analysis.