ゲノム情報科学研究教育機構  アブストラクト
Date September 10, 2012
Speaker Dr. Ryohei Fujimaki, NEC Laboratories America
Title Factorized Asymptotic Bayesian Hidden Markov Models
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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