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Cited by 368

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Regularized Generalized Canonical Correlation Analysis

Open access
Tenenhaus, Arthur, Tenenhaus, Michel·2011·Psychometrika
DOI 10.1007/s11336-011-9206-8
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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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Metadata

Authors
Tenenhaus, A., & Tenenhaus, M.
Venue
Psychometrika
Publisher
Cambridge University Press (CUP)
Published
2011-04
Volume
76
Issue
2
Pages
257-284
ISSN
  • 0033-3123 (ISSN-L)
  • 1860-0980
DOI
10.1007/s11336-011-9206-8
OpenAlex
W1968206427
Cited by
368
References
2
FWCI
13.91
Type
journal article
Language
en
Open access
green
Licence
www.cambridge.org/core/terms (unspecified, 5024 d)

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Tenenhaus, A., & Tenenhaus, M. (2011). Regularized Generalized Canonical Correlation Analysis. Psychometrika. https://doi.org/10.1007/s11336-011-9206-8

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