Bayesian Filtering with Online Gaussian Process Latent Variable Models

Yali Wang, Marcus Brubaker Toyota Technological Institute at Chicago, Brahim Chaib-draa, Raquel Urtasun
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:677-685, 2014.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-wang14a, title = {{B}ayesian Filtering with Online {G}aussian Process Latent Variable Models}, author = {Wang, Yali and Chicago, Marcus Brubaker Toyota Technological Institute at and Chaib-draa, Brahim and Urtasun, Raquel}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {677--685}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/wang14a/wang14a.pdf}, url = {https://proceedings.mlr.press/r12/wang14a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Bayesian Filtering with Online Gaussian Process Latent Variable Models %A Yali Wang %A Marcus Brubaker Toyota Technological Institute at Chicago %A Brahim Chaib-draa %A Raquel Urtasun %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-wang14a %I PMLR %P 677--685 %U https://proceedings.mlr.press/r12/wang14a.html %V R12 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Wang, Y., Chicago, M.B.T.T.I.a., Chaib-draa, B. & Urtasun, R.. (2014). Bayesian Filtering with Online Gaussian Process Latent Variable Models. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:677-685 Available from https://proceedings.mlr.press/r12/wang14a.html. Reissued by PMLR on 04 October 2026.

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