Overlapping Mixtures of Gaussian Processes for the Data Association Problem

Miguel Lázaro-Gredilla; Steven Van Vaerenbergh; Neil D. Lawrence

doi:10.1016/j.patcog.2011.10.004

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Overlapping Mixtures of Gaussian Processes for the Data Association Problem

Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence

Pattern Recognition, 10(4), 2012.

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.

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BibTeX


@Article{Lazaro-overlapping11,
  title = 	 {Overlapping Mixtures of {G}aussian Processes for the Data Association Problem},
  author = 	 {Lázaro-Gredilla, Miguel and Van Vaerenbergh, Steven and Lawrence, Neil D.},
  journal =      {Pattern Recognition},
  year = 	 {2012},
  volume = 	 {10},
  number =       {4},
  doi = 	 {10.1016/j.patcog.2011.10.004},
  url = 	 {/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 Journal Article
%T Overlapping Mixtures of Gaussian Processes for the Data Association Problem
%A Miguel Lázaro-Gredilla
%A Steven Van Vaerenbergh
%A Neil D. Lawrence
%J Pattern Recognition
%D 2012	
%F Lazaro-overlapping11
%R 10.1016/j.patcog.2011.10.004
%U /publications/lazaro-overlapping11.html
%V 10
%N 4
%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  - JOUR
TI  - Overlapping Mixtures of Gaussian Processes for the Data Association Problem
AU  - Miguel Lázaro-Gredilla
AU  - Steven Van Vaerenbergh
AU  - Neil D. Lawrence
DA  - 2012/04/04	
ID  - Lazaro-overlapping11
VL  - 10
IS  - 4
DO  - 10.1016/j.patcog.2011.10.004
UR  - /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.. (2012). Overlapping Mixtures of Gaussian Processes for the Data Association Problem. Pattern Recognition 10(4) doi:10.1016/j.patcog.2011.10.004 Available from /publications/lazaro-overlapping11.html.