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 nonparametric 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 example results are presented, using both Gibbs and variational Bayesian inference, with comparisons to other state-of-the-art approaches.