Flexible Regularized Estimation in High-Dimensional Mixed Membership Models.

ABSTRACT: Mixed membership models are an extension of finite mixture models, where each observation can partially belong to more than one mixture component. A probabilistic framework for mixed membership models of high-dimensional continuous data is proposed with a focus on scalability and interpretability. The novel probabilistic representation of mixed membership is based on convex combinations of dependent multivariate Gaussian random vectors. In this setting, scalability is ensured through approximations of a tensor covariance structure through multivariate eigen-approximations with adaptive regularization imposed through shrinkage priors. Conditional weak posterior consistency is established on an unconstrained model, allowing for a simple posterior sampling scheme while keeping many of the des

SUBMITTER: Marco N 

PROVIDER: S-EPMC11423932 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature