A fast SCCA algorithm for big data analysis in brain imaging genetics

Alzheimer’s Disease Neuroimaging Initiative

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

Mining big data in brain imaging genetics is an emerging topic in brain science. It can uncover meaningful associations between genetic variations and brain structures and functions. Sparse canonical correlation analysis (SCCA) is introduced to discover bi-multivariate correlations with feature selection. However, these SCCA methods cannot be directly applied to big brain imaging genetics data due to two limitations. First, they have cubic complexity in the size of the matrix involved and are computational and memory intensive when the matrix becomes large. Second, the parameters in an SCCA method need to be fine-tuned in advance. This further dramatically increases the computational time, and gets severe in high-dimensional scenarios. In this paper, we propose two fast and efficient algorithms to speed up the structure-aware SCCA (S2CCA) implementations without modification to the original SCCA models. The fast algorithms employ a divide-and-conquer strategy and are easy to implement. The experimental results, compared with conventional algorithms, show that our algorithms reduce the time usage significantly. Specifically, the fast algorithms improve the computational efficiency by tens to hundreds of times compared to conventional algorithms. Besides, our algorithms yield similar correlation coefficients and canonical loading profiles to the conventional implementations. Our fast algorithms can be easily parallelized to further reduce the computational time. This indicates that the proposed fast scalable SCCA algorithms can be a powerful tool for big data analysis in brain imaging genetics.

源语言英语
主期刊名Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics - 1st International Workshop, GRAIL 2017 6th International Workshop, MFCA 2017 and 3rd International Workshop, MICGen 2017 Held in Conjunction with MICCAI 2017, Proceedings
编辑M. Jorge Cardoso, Tal Arbel
出版商Springer Verlag
210-219
页数10
ISBN(印刷版)9783319676746
DOI
出版状态已出版 - 2017
活动1st International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017 and 3rd International Workshop on Imaging Genetics, MICGen 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, 加拿大
期限: 10 9月 201714 9月 2017

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10551 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议1st International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017 and 3rd International Workshop on Imaging Genetics, MICGen 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
国家/地区加拿大
Quebec City
时期10/09/1714/09/17

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