A novel SCCA approach via truncated ℓ1-norm and truncated group lasso for brain imaging genetics.

ABSTRACT:

Motivation

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.

Results

In this

SUBMITTER: Du L 

PROVIDER: S-EPMC5860211 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature