Structured Variable Selection for Regularized Generalized Canonical Correlation Analysis, The Multiple Facets of Partial Least Squares and Related Methods - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Chapitre D'ouvrage Année : 2016

Structured Variable Selection for Regularized Generalized Canonical Correlation Analysis, The Multiple Facets of Partial Least Squares and Related Methods

Tommy Lofstedt
  • Fonction : Auteur
Fouad Hadj-Selem
  • Fonction : Auteur
  • PersonId : 950460
Vincent Guillemot
Vincent Frouin
Arthur Tenenhaus

Résumé

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.
Fichier non déposé

Dates et versions

hal-01396614 , version 1 (14-11-2016)

Identifiants

Citer

Tommy Lofstedt, Fouad Hadj-Selem, Vincent Guillemot, Cathy Philippe, Edouard Duchesnay, et al.. Structured Variable Selection for Regularized Generalized Canonical Correlation Analysis, The Multiple Facets of Partial Least Squares and Related Methods . Springer Proceedings in Mathematics & Statistics, pp.129-139, 2016, The Multiple Facets of Partial Least Squares and Related Methods, ⟨10.1007/978-3-319-40643-5_10⟩. ⟨hal-01396614⟩
298 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More