Nonparametric learning of dictionaries for sparse representation of sensor signals

Mingyuan Zhou, John Paisley, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publicationCAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Pages237-240
Number of pages4
DOIs
StatePublished - Dec 1 2009
Externally publishedYes

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