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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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