Nonparametric learning of dictionaries for sparse representation of sensor signals

Published

Journal Article

Nonparametric Bayesian techniques are considered for learning dictionaries for sparse data representations, with applications in sparse rendering of sensor data. The beta process is employed as a prior for learning the dictionary, and this non parametric method naturally infers an appropriate dictionary size. The proposed method can learn a sparse dictionary, and may also be used to denoise a signal under test. The noise variance need not be known, and can be non-stationary. The dictionary coefficients for a given sensor signal may be employed within a classifier. Several exam pIe results are presented, using both Gibbs and variational Bayesian inference, with comparisons to other state-of-the-art approaches. © 2009 IEEE.

Full Text

Duke Authors

Cited Authors

  • Zhou, M; Paisley, J; Carin, L

Published Date

  • December 1, 2009

Published In

  • Camsap 2009 2009 3rd Ieee International Workshop on Computational Advances in Multi Sensor Adaptive Processing

Start / End Page

  • 237 - 240

Digital Object Identifier (DOI)

  • 10.1109/CAMSAP.2009.5413290

Citation Source

  • Scopus