Flexible Regularized Estimation in High-Dimensional Mixed Membership Models

TL;DR

This paper introduces a scalable, interpretable probabilistic framework for high-dimensional mixed membership models using convex combinations of Gaussian vectors, with applications in biomedical data analysis.

Contribution

It proposes a novel probabilistic representation for mixed membership models that enhances scalability and interpretability in high-dimensional settings.

Findings

Effective in modeling biomedical data such as brain imaging and gene expression.

Ensures posterior consistency with a simple sampling scheme.

Challenges traditional cluster assumptions in complex data.

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 desired theoretical properties of our model. The model is motivated by two biomedical case studies: a case study on functional brain imaging of children with autism spectrum disorder (ASD) and a case study on gene expression data from breast cancer tissue. These applications highlight how the typical assumption made in cluster analysis, that each observation comes from one homogeneous subgroup, may often be restrictive in several applications, leading to unnatural interpretations of data features.