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June 2011 Nonparametric Bayesian sparse factor models with application to gene expression modeling
David Knowles, Zoubin Ghahramani
Ann. Appl. Stat. 5(2B): 1534-1552 (June 2011). DOI: 10.1214/10-AOAS435

Abstract

A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data Y is modeled as a linear superposition, G, of a potentially infinite number of hidden factors, X. The Indian Buffet Process (IBP) is used as a prior on G to incorporate sparsity and to allow the number of latent features to be inferred. The model’s utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for E. Coli, and on three biological data sets of increasing complexity.

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David Knowles. Zoubin Ghahramani. "Nonparametric Bayesian sparse factor models with application to gene expression modeling." Ann. Appl. Stat. 5 (2B) 1534 - 1552, June 2011. https://doi.org/10.1214/10-AOAS435

Information

Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1223.62013
MathSciNet: MR2849785
Digital Object Identifier: 10.1214/10-AOAS435

Keywords: factor analysis , Indian buffet process , Markov chain Monte Carlo , nonparametric Bayes , Sparsity

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.5 • No. 2B • June 2011
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