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September 2014 Joint modeling of multiple time series via the beta process with application to motion capture segmentation
Emily B. Fox, Michael C. Hughes, Erik B. Sudderth, Michael I. Jordan
Ann. Appl. Stat. 8(3): 1281-1313 (September 2014). DOI: 10.1214/14-AOAS742

Abstract

We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our model discovers a latent set of dynamical behaviors shared among the sequences, and segments each time series into regions defined by a subset of these behaviors. Using a beta process prior, the size of the behavior set and the sharing pattern are both inferred from data. We develop Markov chain Monte Carlo (MCMC) methods based on the Indian buffet process representation of the predictive distribution of the beta process. Our MCMC inference algorithm efficiently adds and removes behaviors via novel split-merge moves as well as data-driven birth and death proposals, avoiding the need to consider a truncated model. We demonstrate promising results on unsupervised segmentation of human motion capture data.

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Emily B. Fox. Michael C. Hughes. Erik B. Sudderth. Michael I. Jordan. "Joint modeling of multiple time series via the beta process with application to motion capture segmentation." Ann. Appl. Stat. 8 (3) 1281 - 1313, September 2014. https://doi.org/10.1214/14-AOAS742

Information

Published: September 2014
First available in Project Euclid: 23 October 2014

zbMATH: 1303.62048
MathSciNet: MR3271333
Digital Object Identifier: 10.1214/14-AOAS742

Keywords: Bayesian nonparametrics , beta process , Hidden Markov models , motion capture , multiple time series

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.8 • No. 3 • September 2014
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