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

Dislocation

護(hù)身符

2191-544X des an acceleration algorithm that speeds up the convergent This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data.?.In the part dealing with the principle, after a brief introduction

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Seminar

Joint Dimension Reduction and Clusterings problem, several methods have been proposed that jointly perform clustering of objects and dimension reduction of the variables. In this chapter, we review the technique whereby multiple correspondence analysis and k-means clustering are combined in order to investigate the relationships between qualitative variables.

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Multiple Correspondence Analysisformulate an MCA. We introduce a formulation in which the quantified data matrix is approximated by a lower-rank matrix using the quantification technique proposed by Murakami et al. (Non-metric principal component analysis for categorical variables with multiple quantifications, .).

Lobotomy

Variable Selection in Nonlinear Principal Component Analysis for numerical variables. In this chapter, we discuss variable selection in nonlinear PCA. We select a subset of variables that represents all variables as far as possible from mixed measurement level data using criteria in the modified PCA, which naturally includes a variable selection procedure.