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Keywords
(11)
Composite Structure
Data Augmentation
Em Algorithm
Gaussian Mixture Model
Gibbs Sampling
Linear Regression
Markov Chain Monte Carlo
Nonnegative Matrix Factorization
Nonnegative Matrix Factorization
Proof of Correctness
Space Alternating Generalized Expectation Maximization
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Efficient Markov chain Monte Carlo inference in composite models with space alternating data augmentation
Efficient Markov chain Monte Carlo inference in composite models with space alternating data augmentation,10.1109/SSP.2011.5967665,C. Fevotte,O. Cappe
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Efficient Markov chain Monte Carlo inference in composite models with space alternating data augmentation
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C. Fevotte
,
O. Cappe
,
A. T. Cemgil
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.
Conference:
IEEE/SP Workshop on Statistical Signal Processing  SSP
, pp. 221224, 2011
DOI:
10.1109/SSP.2011.5967665
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References
(6)
NONNEGATIVE MATRIX FACTORIZATIONS AS PROBABILISTIC INFERENCE IN COMPOSITE MODELS
(
Citations: 9
)
Cedric F ´
,
A. Taylan CEMGIL
Published in 2009.
Space alternating data augmentation: application to finite mixture of Gaussians and speaker recognition
(
Citations: 1
)
Arnaud Doucet
,
S. Senecal
,
T. Matsui
Conference:
International Conference on Acoustics, Speech, and Signal Processing  ICASSP
, vol. 4, pp. iv/713iv/716 Vol, 2005
Spacealternating generalized expectationmaximization algorithm
(
Citations: 400
)
Jeffrey A. Fessler
,
A. O. Hero
Journal:
IEEE Transactions on Signal Processing  TSP
, vol. 42, no. 10, pp. 26642677, 1994
Partially Collapsed Gibbs Samplers: Theory and Methods
(
Citations: 12
)
David A. van Dyk
,
Taeyoung Park
Published in 2008.
Sparse Bayesian Learning and the Relevance Vector Machine
(
Citations: 1085
)
Michael E. Tipping
Journal:
Journal of Machine Learning Research  JMLR
, vol. 1, pp. 211244, 2001