Sparse Canonical Correlation Analysis via truncated ℓ1-norm with application to brain imaging genetics
Year: 2016
Type: conference-paper
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 experimental results, compared with two benchmark methods, show that our method identifies better and meaningful canonical loading patterns in both simulated and real imaging genetic analyse.
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: G104586243, Indiana Alzheimer Disease Center, Bioinformatics Strategies for Multidimensional Brain Imaging Genetics, Genetic architecture of memory and executive functioning in Alzheimer's disease, Indiana Clinical and Translational Sciences Institute, Integrative Bioinformatics Approaches to Human Brain Genomics and Connectomics, Alzheimer's Disease Neuroimaging Initiative, Memory Circuitry in MCI and Early Alzheimerâs Disease Prodrome: Molecular Drivers, Amyloid Imaging, VMCI, and Analysis for ADNI, Metabolic Networks and Pathways in Alzheimer's Disease, Alzheimer's Disease Neuroimaging Initiative (ADNI4)
OpenAlex ID: W2578499161