Overlapping Mixtures of Gaussian Processes for the data association problem

PATTERN RECOGNITION

卷:45

Article

Lazaro-Gredilla, Miguel¹  Van Vaerenbergh, Steven¹  Lawrence, Neil D.² 

[1] Univ Cantabria, Dept Commun Engn, E-39005 Santander, Spain

[2] Univ Sheffield, Dept Comp Sci, Machine Learning Grp, Sheffield S1 4DP, S Yorkshire, England

关键词: Gaussian Processes;    Marginalized variational inference;    Bayesian models;   

DOI  :  10.1016/j.patcog.2011.10.004

来源: Elsevier

【 摘 要 】

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. (C) 2011 Elsevier Ltd. All rights reserved.

【 授权许可】

Free