Overlapping Mixtures of Gaussian Processes for the Data Association Problem

Miguel Lázaro Gredilla, Steven Van VaerenberghNeil D. Lawrence
,  10(4), 2011.

Abstract

In this work we introduce a mixture of GPs to address the data association problem, i.e., to label a group of observations according to the sources that generated them. Unlike several previously proposed GP mixtures, the novel mixture has the distinct characteristic of using no gating function to determine the association of samples and mixture components. Instead, all the GPs in the mixture are global and samples are clustered following “trajectories” across input space. We use a non-standard variational Bayesian algorithm to efficiently recover sample labels and learn the hyperparameters. We show how multi-object tracking problems can be disambiguated and also explore the characteristics of the model in traditional regression settings.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-lazaro-overlapping11, title = {Overlapping Mixtures of Gaussian Processes for the Data Association Problem}, author = {Miguel Lázaro Gredilla and Steven Van Vaerenbergh and Neil D. Lawrence}, year = {}, editor = {}, volume = {10}, number = {4}, url = {http://inverseprobability.com/publications/lazaro-overlapping11.html}, abstract = {In this work we introduce a mixture of GPs to address the data association problem, i.e., to label a group of observations according to the sources that generated them. Unlike several previously proposed GP mixtures, the novel mixture has the distinct characteristic of using no gating function to determine the association of samples and mixture components. Instead, all the GPs in the mixture are global and samples are clustered following “trajectories” across input space. We use a non-standard variational Bayesian algorithm to efficiently recover sample labels and learn the hyperparameters. We show how multi-object tracking problems can be disambiguated and also explore the characteristics of the model in traditional regression settings.} }
Endnote
%0 Conference Paper %T Overlapping Mixtures of Gaussian Processes for the Data Association Problem %A Miguel Lázaro Gredilla %A Steven Van Vaerenbergh %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lazaro-overlapping11 %I PMLR %J Proceedings of Machine Learning Research %P -- %R 10.1016/j.patcog.2011.10.004 %U http://inverseprobability.com %V %N 4 %W PMLR %X In this work we introduce a mixture of GPs to address the data association problem, i.e., to label a group of observations according to the sources that generated them. Unlike several previously proposed GP mixtures, the novel mixture has the distinct characteristic of using no gating function to determine the association of samples and mixture components. Instead, all the GPs in the mixture are global and samples are clustered following “trajectories” across input space. We use a non-standard variational Bayesian algorithm to efficiently recover sample labels and learn the hyperparameters. We show how multi-object tracking problems can be disambiguated and also explore the characteristics of the model in traditional regression settings.
RIS
TY - CPAPER TI - Overlapping Mixtures of Gaussian Processes for the Data Association Problem AU - Miguel Lázaro Gredilla AU - Steven Van Vaerenbergh AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-lazaro-overlapping11 PB - PMLR SP - DP - PMLR EP - DO - 10.1016/j.patcog.2011.10.004 L1 - UR - http://inverseprobability.com/publications/lazaro-overlapping11.html AB - In this work we introduce a mixture of GPs to address the data association problem, i.e., to label a group of observations according to the sources that generated them. Unlike several previously proposed GP mixtures, the novel mixture has the distinct characteristic of using no gating function to determine the association of samples and mixture components. Instead, all the GPs in the mixture are global and samples are clustered following “trajectories” across input space. We use a non-standard variational Bayesian algorithm to efficiently recover sample labels and learn the hyperparameters. We show how multi-object tracking problems can be disambiguated and also explore the characteristics of the model in traditional regression settings. ER -
APA
Lázaro Gredilla, M., Van Vaerenbergh, S. & Lawrence, N.D.. (). Overlapping Mixtures of Gaussian Processes for the Data Association Problem. , in PMLR (4):-

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