Regularized Generalized Canonical Correlation Analysis |
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Authors: | Arthur Tenenhaus Michel Tenenhaus |
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Institution: | 1.Department of Signal Processing and Electronics Systems,Supelec, Gif-sur-Yvette,Gif-sur-Yvette cedex,France;2.HEC Paris,Jouy-en-Josas,France |
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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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Keywords: | |
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