Presentation 2011-12-16
Nonparametric Bayesian State Estimation by Detecting Change Points and Sharing Segments on Time Series Data
Masamichi SHIMOSAKA, Yuichi MORIYA, Rui FUKUI, Tomomasa SATO,
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Abstract(in English) In this paper, we propose a novel framework for estimating state spaces where the size is unknown. The proposed framework resolves rapid transitions among redundant states, which are common problem with nonparametric state estimation models. Our approach is based on segmentation and segment categorization, while another approach is sticky extension (Fox et al., 2008). We provide an inference strategy for hidden Markov model and switching linear dynamical system. By using synthetic data and motion capture data, we demonstrate that our method is superior over conventional methods in view of extracting motion sequences and inference efficiency.
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Keyword(in English) Dirichlet process mixtures / hidden Markov model / switching linear dynamical system / segmentation / state estimation
Paper # PRMU2011-145
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Committee PRMU
Conference Date 2011/12/8(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Nonparametric Bayesian State Estimation by Detecting Change Points and Sharing Segments on Time Series Data
Sub Title (in English)
Keyword(1) Dirichlet process mixtures
Keyword(2) hidden Markov model
Keyword(3) switching linear dynamical system
Keyword(4) segmentation
Keyword(5) state estimation
1st Author's Name Masamichi SHIMOSAKA
1st Author's Affiliation Graduate School of Information Science and Engineering, the University of Tokyo()
2nd Author's Name Yuichi MORIYA
2nd Author's Affiliation Graduate School of Information Science and Engineering, the University of Tokyo
3rd Author's Name Rui FUKUI
3rd Author's Affiliation Graduate School of Information Science and Engineering, the University of Tokyo
4th Author's Name Tomomasa SATO
4th Author's Affiliation Graduate School of Information Science and Engineering, the University of Tokyo
Date 2011-12-16
Paper # PRMU2011-145
Volume (vol) vol.111
Number (no) 353
Page pp.pp.-
#Pages 6
Date of Issue