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Expandable factor analysis

Author(s): Srivastava, Sanvesh; Engelhardt, Barbara E; Dunson, David B

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Abstract: Bayesian sparse factor models have proven useful for characterizing dependence in multivariate data, but scaling computation to large numbers of samples and dimensions is problematic. We propose expandable factor analysis for scalable inference in factor models when the number of factors is unknown. The method relies on a continuous shrinkage prior for efficient maximum a posteriori estimation of a low-rank and sparse loadings matrix. The structure of the prior leads to an estimation algorithm that accommodates uncertainty in the number of factors. We propose an information criterion to select the hyperparameters of the prior. Expandable factor analysis has better false discovery rates and true positive rates than its competitors across diverse simulation settings. We apply the proposed approach to a gene expression study of ageing in mice, demonstrating superior results relative to four competing methods.
Publication Date: 2017
Citation: Srivastava, Sanvesh, Barbara E. Engelhardt, and David B. Dunson. "Expandable factor analysis." Biometrika 104, no. 3 (2017): 649-663. doi:10.1093/biomet/asx030
DOI: 10.1093/biomet/asx030
ISSN: 0006-3444
EISSN: 1464-3510
Pages: 649 - 663
Type of Material: Journal Article
Journal/Proceeding Title: Biometrika
Version: Author's manuscript

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