Journal
PSYCHOMETRIKA
Volume 76, Issue 2, Pages 257-284Publisher
SPRINGER
DOI: 10.1007/s11336-011-9206-8
Keywords
generalized canonical correlation analysis; multi-block data analysis; PLS path modeling; regularized canonical correlation analysis
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Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to three or more sets of variables. It constitutes a general framework for many multi-block data analysis methods. It combines the power of multi-block data analysis methods (maximization of well identified criteria) and the flexibility of PLS path modeling (the researcher decides which blocks are connected and which are not). Searching for a fixed point of the stationary equations related to RGCCA, a new monotonically convergent algorithm, very similar to the PLS algorithm proposed by Herman Wold, is obtained. Finally, a practical example is discussed.
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