Sparse linear discriminant analysis for multiview structured data.

ABSTRACT: Classification methods that leverage the strengths of data from multiple sources (multiview data) simultaneously have enormous potential to yield more powerful findings than two-step methods: association followed by classification. We propose two methods, sparse integrative discriminant analysis (SIDA), and SIDA with incorporation of network information (SIDANet), for joint association and classification studies. The methods consider the overall association between multiview data, and the separation within each view in choosing discriminant vectors that are associated and optimally separate subjects into different classes. SIDANet is among the first methods to incorporate prior structural information in joint association and classification studies. It uses the normalized Laplacian of a gra

SUBMITTER: Safo SE 

PROVIDER: S-EPMC8906173 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature