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 prediction and observation models are learned in an online fashion. Our approach is able to handle 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 estimation process, we explore two computationally efficient GP variants, sparse online GP and local GP, which help to manage computation requirements for each mixture component. Our experiments demonstrate that our approach can track human motion much more accurately than existing approaches that learn the prediction and observation 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 prediction and observation models are learned in an online fashion. Our approach is able to handle 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 estimation process, we explore two computationally efficient GP variants, sparse online GP and local GP, which help to manage computation requirements for each mixture component. Our experiments demonstrate that our approach can track human motion much more accurately than existing approaches that learn the prediction and observation 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 prediction and observation models are learned in an online fashion. Our approach is able to handle 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 estimation process, we explore two computationally efficient GP variants, sparse online GP and local GP, which help to manage computation requirements for each mixture component. Our experiments demonstrate that our approach can track human motion much more accurately than existing approaches that learn the prediction and observation 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.

Related Material