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 language||English (US)|
|Title of host publication||CAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing|
|Number of pages||4|
|State||Published - Dec 1 2009|