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Structured Variable Selection for Regularized Generalized Canonical Correlation Analysis

  • Tommy LöfstedtEmail author
  • Fouad Hadj-Selem
  • Vincent Guillemot
  • Cathy Philippe
  • Edouard Duchesnay
  • Vincent Frouin
  • Arthur Tenenhaus
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 173)

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.

Keywords

RGCCA Variable selection Structured penalty Sparse penalty 

Notes

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Tommy Löfstedt
    • 1
    Email author
  • Fouad Hadj-Selem
    • 2
  • Vincent Guillemot
    • 3
  • Cathy Philippe
    • 4
  • Edouard Duchesnay
    • 2
  • Vincent Frouin
    • 2
  • Arthur Tenenhaus
    • 5
  1. 1.Computational Life Science Cluster (CLiC), Department of ChemistryUmeå UniversityUmeåSweden
  2. 2.NeuroSpinCEA SaclayGif-sur-YvetteFrance
  3. 3.Bioinformatics/Biostatistics Core Facility, IHU-A-ICMBrain and Spine InstituteParisFrance
  4. 4.Gustave RoussyVillejuifFrance
  5. 5.Laboratoire des Signaux et Systèmes (L2S, UMR CNRS 8506)CentraleSupélec-CNRS-Université Paris-Sud and Bioinformatics/Biostatistics Platform IHU-A-ICM, Brain and Spine InstituteParisFrance

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