Bayesian mixture of gaussian processes for data association problem

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We address the data association problem and propose a Bayesian approach based on a mixture of Gaus-sian Processes (GPs) having two key components, the assignment probabilities and the GPs. In the pro-posed approach, the two key components are simultaneously updated according to observations through an efficient Expectation-Maximization (EM) algorithm that we develop. The proposed approach is thus more adaptive to the observations than the existing approaches for data association. To validate the per-formance of the proposed approach, we provide experimental results with real data sets as well as two synthetic data sets. We also provide a theoretical analysis to show the effectiveness of the Bayesian up -date.(c) 2022 Elsevier Ltd. All rights reserved.
Publisher
ELSEVIER SCI LTD
Issue Date
2022-07
Language
English
Article Type
Article
Citation

PATTERN RECOGNITION, v.127

ISSN
0031-3203
DOI
10.1016/j.patcog.2022.108592
URI
http://hdl.handle.net/10203/295865
Appears in Collection
MA-Journal Papers(저널논문)
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