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.
Keywords
Algorithms
Artificial intelligence
Computation theory
Inverse problems
Learning systems
Light sources
Stochastic models
Stochastic systems
Sparse regression
Stochastic decomposition
Noisy observations
ILL-posed inverse problem
Signal processing
Quantum homodyne tomography
Coorbit Theory
Bayesian nonparametrics