Signal stochastic decomposition over continuous dictionaries

Abstract

We propose a Bayesian nonparametrics method, including algorithm for posterior computation, for the sparse regression problem. Our method applies in a general setting, when there are direct or indirect noisy observations of the signal. We try to make a wide focus on smoothness properties and sparsity of the approximate. As an example, we consider the ill-posed inverse problem of Quantum Homodyne Tomography.

Publication
In 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Date
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