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- Title
Regularized Generalized Canonical Correlation Analysis.
- Authors
TENENHAUS, ARTHUR; TENENHAUS, MICHEL
- Abstract
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.
- Subjects
STATISTICAL correlation; PROBABILITY theory; GRAPHIC methods in statistics; DATA analysis; EQUATIONS
- Publication
Psychometrika, 2011, Vol 76, Issue 2, p257
- ISSN
0033-3123
- Publication type
Article
- DOI
10.1007/s11336-011-9206-8