Sparse Canonical Correlation Analysis via truncated ℓ1-norm with application to brain imaging genetics

Lei Du, Tuo Zhang, Kefei Liu, Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Lei Guo, Andrew Saykin, Li Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Discovering bi-multivariate associations between genetic markers and neuroimaging quantitative traits is a major task in brain imaging genetics. Sparse Canonical Correlation Analysis (SCCA) is a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ1-norm or its variants. The ℓ0-norm is more desirable, which however remains unexplored since the ℓ0-norm minimization is NP-hard. In this paper, we impose the truncated ℓ1-norm to improve the performance of the ℓ1-norm based SCCA methods. Besides, we propose two efficient optimization algorithms and prove their convergence. The experimental results, compared with two benchmark methods, show that our method identifies better and meaningful canonical loading patterns in both simulated and real imaging genetic analyse.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages707-711
Number of pages5
ISBN (Electronic)9781509016105
DOIs
StatePublished - Jan 17 2017
Event2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China
Duration: Dec 15 2016Dec 18 2016

Other

Other2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
CountryChina
CityShenzhen
Period12/15/1612/18/16

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Keywords

  • Brain Imaging Genetics
  • Sparse Canonical Correlation Analysis
  • Truncated ℓ-norm

ASJC Scopus subject areas

  • Genetics
  • Medicine (miscellaneous)
  • Genetics(clinical)
  • Biochemistry, medical
  • Biochemistry
  • Molecular Medicine
  • Health Informatics

Cite this

Du, L., Zhang, T., Liu, K., Yao, X., Yan, J., Risacher, S. L., Guo, L., Saykin, A., & Shen, L. (2017). Sparse Canonical Correlation Analysis via truncated ℓ1-norm with application to brain imaging genetics. In Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 707-711). [7822605] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822605