Titlebook: Nonlinear Principal Component Analysis and Its Applications; Yuichi Mori,Masahiro Kuroda,Naomichi Makino Book 2016 The Author(s) 2016 Alte

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https://doi.org/10.1007/978-981-10-0159-8Alternating Least Squares; Mixed Measurement Level Data; Multiple Correspondence Analysis; Nonlinear PC

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Nonlinear Principal Component Analysis mixed measurement level data. For the extended PCA with such a mixture of quantitative and qualitative data, we require the quantification of qualitative data in order to obtain optimal scaling data. PCA with optimal scaling is referred to as nonlinear PCA, (Gifi, Nonlinear Multivariate Analysis. W

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Sparse Multiple Correspondence Analysishe rotation technique is widely used for this purpose. However, an alternative approach, called sparse MCA, has also been proposed. One of the advantages of sparse MCA is that, in contrast to unrotated or rotated ordinary MCA loadings, some loadings in sparse MCA can be exactly zero. A real data exa

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Acceleration of Convergence of the Alternating Least Squares Algorithm for Nonlinear Principal Compoeen optimal scaling for quantifying qualitative data and the analysis of optimally scaled data using the ordinary PCA approach. PRINCIPALS of Young et al. (Psychometrika 43:279–281) (.) and PRINCALS of Gifi (Nonlinear Multivariate Analysis. Wiley, Chichester) (.) are the ALS algorithms used for nonl

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public.Argues that the Israeli–Palestinian dispute consists.This book explains why the Israeli–Palestinian dispute is so difficult to resolve by showing that it consists of multiple distinct conflicts. Because these tend to be conflated into a single conflict, attempts at peace have not worked. Und

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