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Regularized generalized canonical correlation analysis

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.
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Contributor : Karine El Rassi Connect in order to contact the contributor
Submitted on : Wednesday, June 29, 2011 - 10:38:26 AM
Last modification on : Monday, December 14, 2020 - 12:38:04 PM
Long-term archiving on: : Friday, September 30, 2011 - 2:21:41 AM


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  • HAL Id : hal-00604496, version 1



Arthur Tenenhaus, Michel Tenenhaus. Regularized generalized canonical correlation analysis. Psychometrika, Springer Verlag, 2011, 76 (2), pp.257-284. ⟨hal-00604496⟩



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