Acceleration of the alternating least squares algorithm for principal components analysis

Principal components analysis (PCA) is a popular descriptive multivariate method for handling quantitative data and it can be extended to deal with qualitative data and mixed measurement level data. The existing algorithms for extended PCA are PRINCIPALS of Young et al. (1978) and PRINCALS of Gifi (...

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Bibliographic Details
Published in:	Computational statistics & data analysis Vol. 55; no. 1; pp. 143 - 153
Main Authors:	Kuroda, Masahiro, Mori, Yuichi, Iizuka, Masaya, Sakakihara, Michio
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 2011 Elsevier
Series:	Computational Statistics & Data Analysis
Subjects:	Acceleration Acceleration of convergence Algorithms Alternating least squares algorithm Alternating least squares algorithm Vector [epsilon] algorithm Acceleration of convergence PRINCIPALS PRINCALS Convergence Exact sciences and technology General topics Least squares method Mathematical analysis Mathematical models Mathematics Multivariate analysis Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics PRINCALS Principal component analysis PRINCIPALS Probability and statistics Sciences and techniques of general use Statistics Vector [formula omitted] algorithm Vector ε algorithm Alternating least squares algorithm PRINCIPALS PRINCALS Acceleration of convergence Data analysis Convergence acceleration Estimation Iterative method Iteration Multivariate analysis Algorithm Convergence Convergence speed Selection problem Numerical analysis Statistical computation Least squares method Algorithm analysis Principal component analysis Variable selection
ISSN:	0167-9473, 1872-7352
Online Access:	Get full text
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