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Regularized generalized canonical correlation analysis for multiblock or multigroup data analysis

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  • Tenenhaus, Arthur
  • Tenenhaus, Michel

Abstract

This paper presents an overview of methods for the analysis of data structured in blocks of variables or in groups of individuals. More specifically, regularized generalized canonical correlation analysis (RGCCA), which is a unifying approach for multiblock data analysis, is extended to be also a unifying tool for multigroup data analysis. The versatility and usefulness of our approach is illustrated on two real datasets.

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  • Tenenhaus, Arthur & Tenenhaus, Michel, 2014. "Regularized generalized canonical correlation analysis for multiblock or multigroup data analysis," European Journal of Operational Research, Elsevier, vol. 238(2), pages 391-403.
    • Handle: RePEc:eee:ejores:v:238:y:2014:i:2:p:391-403
    DOI: 10.1016/j.ejor.2014.01.008
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    Cited by:

    1. Tenenhaus, Arthur & Philippe, Cathy & Frouin, Vincent, 2015. "Kernel Generalized Canonical Correlation Analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 114-131.
    2. Marian DÂRDALĂ & Titus Felix FURTUNĂ & Cristian IONIȚĂ, 2017. "Statistical Methods for Analyzing the Relationship among Ethnic, Religious and Linguistic Diversity in Romania," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(4), pages 37-54.
    3. Michel Tenenhaus & Arthur Tenenhaus & Patrick J. F. Groenen, 2017. "Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 737-777, September.

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