Sparse Canonical Correlation Analysis via Truncated ℓ₁-norm with Application to Brain Imaging Genetics.

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 exp

SUBMITTER: Du L 

PROVIDER: S-EPMC5627624 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature