Efficient Markov chain Monte Carlo inference in composite models with space alternating data augmentation

Space alternating data augmentation (SADA) was proposed by Doucet et al (2005) as a MCMC generalization of the SAGE algorithm of Fessler and Hero (1994), itself a famous variant of the EM algorithm. While SADA had previously been applied to inference in Gaussian mixture models, we show this sampler...

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Bibliographic Details
Main Authors: Fevotte, C., Cappe, O., Cemgil, A. T.
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Convergence
Data models
Linear regression
Markov chain Monte Carlo (MCMC)
Markov processes
Monte Carlo methods
Noise
non-negative matrix factorization (NMF)
space alternating data augmentation (SADA)
space alternating generalized expectation-maximization (SAGE)
sparse linear regression
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Summary:Space alternating data augmentation (SADA) was proposed by Doucet et al (2005) as a MCMC generalization of the SAGE algorithm of Fessler and Hero (1994), itself a famous variant of the EM algorithm. While SADA had previously been applied to inference in Gaussian mixture models, we show this sampler to be particularly well suited for models having a composite structure, i.e., when the data may be written as a sum of latent components. The SADA sampler is shown to have favorable mixing properties and lesser storage requirement when compared to standard Gibbs sampling. We provide new alternative proofs of correctness of SADA and report results on sparse linear regression and nonnegative matrix factorization.
ISSN:2373-0803
2693-3551
DOI:10.1109/SSP.2011.5967665