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  Inference algorithms and learning theory for Bayesian sparse factor analysis

Rattray, M., Stegle, O., Sharp, K., & Winn, J. (2009). Inference algorithms and learning theory for Bayesian sparse factor analysis. Bristol, UK: Institute of Physics. doi:10.1088/1742-6596/197/1/012002.

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 Creators:
Rattray, M, Author
Stegle, O1, 2, Author              
Sharp, K, Author
Winn, J, Author
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528696              

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 Abstract: Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

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 Dates: 2009-12
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1088/1742-6596/197/1/012002
BibTex Citekey: 6296
 Degree: -

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Title: International Workshop on Statistical-Mechanical Informatics 2009 (IW-SMI 2009)
Place of Event: Kyoto, Japan
Start-/End Date: 2009-09-13 - 2009-09-16

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Title: Journal of Physics: Conference Series
Source Genre: Series
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Publ. Info: Bristol, UK : Institute of Physics
Pages: - Volume / Issue: 197 (1) Sequence Number: 012002 Start / End Page: 1 - 10 Identifier: -