Projects per year
Abstract
We introduce and analyse a flexible and efficient implementation of Bayesian dictionary learning for sparse coding. By placing Gaussian-inverse-Gamma hierarchical priors on the coefficients, the model can automatically determine the required sparsity level for good reconstructions, whilst also automatically learning the noise level in the data, obviating the need for heuristic methods for choosing sparsity levels. This model can be solved efficiently using Variational Message Passing (VMP), which we have implemented in the Infer.NET framework for probabilistic programming and inference. We analyse the properties of the model via empirical validation on several accelerometer datasets. We provide source code to replicate all of the experiments in this paper.
Original language | English |
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Title of host publication | 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings |
Subtitle of host publication | Proceedings of a meeting held 13-16 September 2016, Vietri sul Mare (Salerno), Italy |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Number of pages | 6 |
ISBN (Electronic) | 9781509007462 |
ISBN (Print) | 9781509007479 |
DOIs | |
Publication status | Published - Dec 2016 |
Event | 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy Duration: 13 Sept 2016 → 16 Sept 2016 |
Publication series
Name | Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) |
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Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISSN (Print) | 1551-2541 |
Conference
Conference | 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings |
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Country/Territory | Italy |
City | Vietri sul Mare, Salerno |
Period | 13/09/16 → 16/09/16 |
Research Groups and Themes
- Jean Golding
- SPHERE
Keywords
- Accelerometers
- Bayesian
- Dictionary Learning
- Sparse Coding
Fingerprint
Dive into the research topics of 'BDL.NET: Bayesian dictionary learning in Infer.NET'. Together they form a unique fingerprint.Projects
- 1 Finished
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SPHERE (EPSRC IRC)
Craddock, I. J. (Principal Investigator), Coyle, D. T. (Principal Investigator), Flach, P. A. (Principal Investigator), Kaleshi, D. (Principal Investigator), Mirmehdi, M. (Principal Investigator), Piechocki, R. J. (Principal Investigator), Stark, B. H. (Principal Investigator), Ascione, R. (Co-Principal Investigator), Ashburn, A. M. (Collaborator), Burnett, M. E. (Collaborator), Damen, D. (Co-Principal Investigator), Gooberman-Hill, R. (Principal Investigator), Harwin, W. S. (Collaborator), Hilton, G. (Co-Principal Investigator), Holderbaum, W. (Collaborator), Holley, A. P. (Manager), Manchester, V. A. (Administrator), Meller, B. J. (Other ), Stack, E. (Collaborator) & Gilchrist, I. D. (Principal Investigator)
1/10/13 → 30/09/18
Project: Research, Parent