Abstract

Parametric dictionaries can increase the ability of sparse representations to meaningfully capture and interpret the underlying signal information, such as encountered in biomedical problems. Given a mapping function from the atom parameter space to the actual atoms, we propose a sparse Bayesian framework for learning the atom parameters, because of its ability to provide full posterior estimates, take uncertainty into account and generalize on unseen data. Inference is performed with Markov Chain Monte Carlo, that uses block sampling to generate the variables of the Bayesian problem. Since the parameterization of dictionary atoms results in posteriors that cannot be analytically computed, we use a Metropolis–Hastings–within-Gibbs framework, according to which variables with closed-form posteriors are generated with the Gibbs sampler, while the remaining ones with the Metropolis Hastings from …

Date

2016

Authors

Theodora Chaspari, Andreas Tsiartas, Panagiotis Tsilifis, Shrikanth S Narayanan

Journal

IEEE Transactions on Signal Processing

Volume

64

Issue

12

Pages

3077-3092

Publisher

IEEE