Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method.

ABSTRACT:

Motivation

Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated.

Results

We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has

SUBMITTER: Du L 

PROVIDER: S-EPMC4907375 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature