Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis.

ABSTRACT: Factor analysis provides a canonical framework for imposing lower-dimensional structure such as sparse covariance in high-dimensional data. High-dimensional data on the same set of variables are often collected under different conditions, for instance in reproducing studies across research groups. In such cases, it is natural to seek to learn the shared versus condition-specific structure. Existing hierarchical extensions of factor analysis have been proposed, but face practical issues including identifiability problems. To address these shortcomings, we propose a class of SUbspace Factor Analysis (SUFA) models, which characterize variation across groups at the level of a lower-dimensional subspace. We prove that the proposed class of SUFA models lead to identifiability of the shared versu

SUBMITTER: Chandra NK 

PROVIDER: S-EPMC12404275 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature