Uncertainty in Artificial Intelligence
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Bayesian Filtering with Online Gaussian Process Latent Variable Models
Yali Wang, Marcus Brubaker, Brahim Chaib-draa, Raquel Urtasun
Abstract:
In this paper we present a novel non-parametric approach to Bayesian filtering, where the predic- tion and observation models are learned in an online fashion. Our approach is able to han- dle multimodal distributions over both models by employing a mixture model representation with Gaussian Processes (GP) based components. To cope with the increasing complexity of the esti- mation process, we explore two computationally efficient GP variants, sparse online GP and local GP, which help to manage computation require- ments for each mixture component. Our exper- iments demonstrate that our approach can track human motion much more accurately than exist- ing approaches that learn the prediction and ob- servation models offline and do not update these models with the incoming data stream.
Keywords:
Pages: 849-857
PS Link:
PDF Link: /papers/14/p849-wang.pdf
BibTex:
@INPROCEEDINGS{Wang14,
AUTHOR = "Yali Wang and Marcus Brubaker and Brahim Chaib-draa and Raquel Urtasun",
TITLE = "Bayesian Filtering with Online Gaussian Process Latent Variable Models",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "849--857"
}


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