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Activity Number: 72
Type: Contributed
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #316781 View Presentation
Title: Structured Bayesian Group Factor Analysis
Author(s): Shiwen Zhao* and Chuan Gao and Sayan Mukherjee and Barbara Engelhardt
Companies: and Duke University and Duke University and Princeton University
Keywords: Bayesian Structured Sparsity ; Canonical Correlation Analysis ; Sparse Priors ; Mixture Models ; Parameter Expansion
Abstract:

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for an observation matrix with p features across n samples. We develop a structured Bayesian group factor analysis model named BASS (Bayesian group factor Analysis with Structured Sparsity)that extends the factor model to multiple coupled observation matrices. Our model puts a structured Bayesian hierarchical prior on the joint factor loading matrix, which achieves shrinkage effect at both a local level (element-wise shrinkage) and a factor level (column-wise shrinkage). In addition, our model separates dense and sparse factors which allows the combination low rank approximation with interpretability. Parameter estimation is achieved through a fast parameter expanded EM algorithm with rotation parameters inducing desirable sparsity. We validate our model on both simulated data with substantial structure and real data sets.


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