Date |
September 10, 2012 |
Speaker |
Dr. Ryohei Fujimaki, NEC Laboratories America
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Title |
Factorized Asymptotic Bayesian Hidden Markov Models
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Abstract |
This talk presents a new model selection method for hidden Markov
models (HMMs), using factorized asymptotic Bayesian inference (FAB).
FAB for HMMs is derived as an iterative lower bound maximization
algorithm of a factorized information criterion (FIC), and has several
desirable properties for learning HMMs, such as asymptotic consistency
of FIC with marginal log-likelihood, a shrinkage effect for hidden state
selection, monotonic increase of the lower FIC bound through the iterative
optimization. Further, it does not have a tunable hyper-parameter,
and thus its model selection process can be fully automated.
Experimental results shows that FAB outperforms states-of-the-art
variational Bayesian HMM and non-parametric Bayesian HMM in
terms of model selection accuracy and computational efficiency.
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