Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors
- Submitting institution
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The University of Bath
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 203107513
- Type
- D - Journal article
- DOI
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10.1109/TSP.2018.2858207
- Title of journal
- IEEE Transactions on Signal Processing
- Article number
- -
- First page
- 4598
- Volume
- 66
- Issue
- 17
- ISSN
- 1053-587X
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in the top journal in signal processing, this paper proposes a novel model for structured sparse regression. In contrast to the previous state-of-the-arts, this model decouples spatial and temporal priors allowing to apply different structural assumptions for the two domains. This decoupling makes the model more memory efficient and allows the online inference of the latent signal for streaming data that is also proposed in the paper. The thorough experiments show the efficacy of the model in a various applications such as video compressive sensing and electroencephalography source localisation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -