Acceleration of the alternating least squares algorithm for principal components analysis

Masahiro Kuroda, Yuichi Mori, Masaya Iizuka and Michio Sakakihara

Computational Statistics & Data Analysis, 2011, vol. 55, issue 1, 143-153

Abstract: 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 (1989) in which the alternating least squares algorithm is utilized. These algorithms based on the least squares estimation may require many iterations in their application to very large data sets and variable selection problems and may take a long time to converge. In this paper, we derive a new iterative algorithm for accelerating the convergence of PRINCIPALS and PRINCALS by using the vector [epsilon] algorithm of Wynn (1962). The proposed acceleration algorithm speeds up the convergence of the sequence of the parameter estimates obtained from PRINCIPALS or PRINCALS. Numerical experiments illustrate the potential of the proposed acceleration algorithm.

Keywords: Alternating; least; squares; algorithm; Vector; [epsilon]; algorithm; Acceleration; of; convergence; PRINCIPALS; PRINCALS (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:1:p:143-153

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