Abstract
Regularized Generalized Canonical Correlation Analysis (RGCCA) extends regularized canonical correlation analysis to more than two sets of variables. Sparse GCCA (SGCCA) was recently proposed to address the issue of variable selection. However, the variable selection scheme offered by SGCCA is limited to the covariance (τ = 1) link between blocks. In this paper we go beyond the covariance link by proposing an extension of SGCCA for the full RGCCA model (τ ∈ [0, 1]). In addition, we also propose an extension of SGCCA that exploits pre-given structural relationships between variables within blocks. Specifically, we propose an algorithm that allows structured and sparsity-inducing penalties to be included in the RGCCA optimization problem.
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Acknowledgements
This work was supported by grants from the French National Research Agency: ANR IA BRAINOMICS (ANR-10-BINF-04), and a European Commission grant: MESCOG (FP6 ERA-NET NEURON 01 EW1207).
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Löfstedt, T. et al. (2016). Structured Variable Selection for Regularized Generalized Canonical Correlation Analysis. In: Abdi, H., Esposito Vinzi, V., Russolillo, G., Saporta, G., Trinchera, L. (eds) The Multiple Facets of Partial Least Squares and Related Methods. PLS 2014. Springer Proceedings in Mathematics & Statistics, vol 173. Springer, Cham. https://doi.org/10.1007/978-3-319-40643-5_10
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DOI: https://doi.org/10.1007/978-3-319-40643-5_10
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