BDL.NET: Bayesian dictionary learning in Infer.NET

Tom Diethe, Niall Twomey, Peter Flach

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)
271 Downloads (Pure)

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 languageEnglish
Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Subtitle of host publicationProceedings of a meeting held 13-16 September 2016, Vietri sul Mare (Salerno), Italy
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781509007462
ISBN (Print)9781509007479
DOIs
Publication statusPublished - Dec 2016
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: 13 Sep 201616 Sep 2016

Publication series

NameProceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1551-2541

Conference

Conference26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
CountryItaly
CityVietri sul Mare, Salerno
Period13/09/1616/09/16

Structured keywords

  • Jean Golding

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

    SPHERE (EPSRC IRC)

    Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Aldamen, D., Gooberman-Hill, R. J. S., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.

    1/10/1330/09/18

    Project: Research, Parent

    Cite this

    Diethe, T., Twomey, N., & Flach, P. (2016). BDL.NET: Bayesian dictionary learning in Infer.NET. In 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings: Proceedings of a meeting held 13-16 September 2016, Vietri sul Mare (Salerno), Italy [7738851] (Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/MLSP.2016.7738851