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Structured Variable Selection for Regularized Generalized Canonical Correlation Analysis
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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2016 (English)In: MULTIPLE FACETS OF PARTIAL LEAST SQUARES AND RELATED METHODS / [ed] Abdi, H Vinzi, VE Russolillo, G Saporta, G Trinchera, L, SPRINGER INT PUBLISHING AG , 2016, Vol. 173, 129-139 p.Conference paper, Published paper (Refereed)
Abstract [en]

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 (tau = 1) link between blocks. In this paper we go beyond the covariance link by proposing an extension of SGCCA for the full RGCCA model. (tau epsilon [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.

Place, publisher, year, edition, pages
SPRINGER INT PUBLISHING AG , 2016. Vol. 173, 129-139 p.
Series
Springer Proceedings in Mathematics & Statistics, ISSN 2194-1009 ; 173
Keyword [en]
RGCCA, Variable selection, Structured penalty, Sparse penalty
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-136099DOI: 10.1007/978-3-319-40643-5_10ISI: 000400840200010ISBN: 978-3-319-40643-5 (print)ISBN: 978-3-319-40641-1 (print)OAI: oai:DiVA.org:umu-136099DiVA: diva2:1109030
Conference
8th Meeting on Partial Least Squares (PLS), MAY 26-28, 2014, Conservatoire Natl Arts Metiers, Paris, FRANCE
Available from: 2017-06-13 Created: 2017-06-13 Last updated: 2017-06-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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