Kuzin, D., Isupova, O. and Mihaylova, L. orcid.org/0000-0001-5856-2223 (2018) Spatio-Temporal Structured Sparse Regression With Hierarchical Gaussian Process Priors. IEEE Transactions on Signal Processing, 66 (17). pp. 4598-4611. ISSN 1053-587X
Abstract
This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that gives a time-evolving representation of the interdependencies between the components of the sparse signal of interest. A hierarchical Gaussian process describes such structure and the interdependencies are represented via the covariance matrices of the prior distributions. The inference is based on the expectation propagation method and the theoretical derivation of the posterior distribution is provided in this paper. The inference framework is thoroughly evaluated over synthetic, real video, and electroencephalography (EEG) data where the spatio-temporal evolving patterns need to be reconstructed with high accuracy. It is shown that it achieves 15% improvement of the F-measure compared with the alternating direction method of multipliers, spatio-temporal sparse Bayesian learning method and the one-level Gaussian process model. Additionally, the required memory for the proposed algorithm is less than in the one-level Gaussian process model. This structured sparse regression framework is of broad applicability to source localization and object detection problems with sparse signals.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Compressed sensing; Gaussian processes; bayes methods; biomedical imaging; object detection |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 TRAX - 607400 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Sep 2018 09:53 |
Last Modified: | 24 Sep 2018 09:30 |
Published Version: | https://doi.org/10.1109/TSP.2018.2858207 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/TSP.2018.2858207 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135555 |