Bayesian Group Factor Analysis

Abstract

We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor analysis does. A group may correspond to one view of the same set of objects, one of many data sets tied by co-occurrence, or a set of alternative variables collected from statistics tables to measure one property of interest. We show that by assuming group-wise sparse factors, active in a subset of the sets, the variation can be decomposed into factors explaining relationships between the sets and factors explaining away set-specific variation. We formulate the assumptions in a Bayesian model which provides the factors, and apply the model to two data analysis tasks, in neuroimaging and chemical systems biology.

9 pages, 5 figures

References in corpus (4)

• Structured Sparse Principal Component Analysis
• A group model for stable multi-subject ICA on fMRI datasets
• Nonparametric Bayesian sparse factor models with application to gene expression modeling
• The Infinite Hierarchical Factor Regression Model

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• Identification of structural features in chemicals associated with cancer drug response: A systematic data-driven analysis
• Learning Sparsity and Block Diagonal Structure in Multi-View Mixture Models