A novel SCCA approach via truncated ℓ 1-norm and truncated group lasso for brain imaging genetics
Abstract
Brain imaging genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been 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 to induce sparsity. The ℓ0-norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 18, 2017
- Source ID
- 10.1093/bioinformatics/btx594
Entities
People
- Andrew J. Saykin
- For The Alzheimer’s Disease Neuroimaging Initiative*
- Jingwen Yan
- Junwei Han
- Kefei Liu
- Lei Du
- Lei Guo
- Li Shen
- Shannon L. Risacher
- Tuo Zhang
- Xiaohui Yao
Organizations
- China Postdoctoral Science Foundation
- Indiana University
- National Institutes of Health
- National Natural Science Foundation of China
- Northwestern Polytechnical University
- United States Department of Defense