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Higher-order solutions for generalized canonical correlation analysis
Communications for Statistical Applications and Methods 2019;26:305-313
Published online May 31, 2019
© 2019 Korean Statistical Society.

Hyuncheol Kang1,a

aDivision of Big Data and Management Engineering, Hoseo University, Korea
Correspondence to: 1Division of Big Data and Management Engineering, Hoseo University, 20, Hoseo-ro 79beon-gil, Baebang-eup, Asan-si, Chungcheongnam-do 31499, Korea.
E-mail: hychkang@hoseo.edu
Received February 27, 2019; Revised March 27, 2019; Accepted March 27, 2019.
Generalized canonical correlation analysis (GCCA) extends the canonical correlation analysis (CCA) to the case of more than two sets of variables and there have been many studies on how two-set canonical solutions can be generalized. In this paper, we derive certain stationary equations which can lead the higher-order solutions of several GCCA methods and suggest a type of iterative procedure to obtain the canonical coefficients. In addition, with some numerical examples we present the methods for graphical display, which are useful to interpret the GCCA results obtained.
Keywords : generalized canonical correlation analysis, higher-order solutions, canonical weights, goodness of approximation indices, canonical loadings, explained variance indices