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 J. Saykin, Li Shen

科研成果: 书/报告/会议事项章节会议稿件同行评审

7 引用 (Scopus)

摘要

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.

源语言英语
主期刊名Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
编辑Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
出版商Institute of Electrical and Electronics Engineers Inc.
707-711
页数5
ISBN(电子版)9781509016105
DOI
出版状态已出版 - 17 1月 2017
活动2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, 中国
期限: 15 12月 201618 12月 2016

出版系列

姓名Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

会议

会议2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
国家/地区中国
Shenzhen
时期15/12/1618/12/16

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