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Conference papers
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 :
Signal processing
Quantum homodyne tomography
Bayesian nonparametrics
Coorbit Theory
ILL-posed inverse problem
Noisy observations
Artificial intelligence
Computation theory
Inverse problems
Learning systems
Light sources
Stochastic models
Stochastic systems
Sparse regression
Stochastic decomposition
Algorithms
https://hal-cea.archives-ouvertes.fr/cea-01830639
Contributor : Léna Le Roy <>
Submitted on : Thursday, July 5, 2018 - 11:29:16 AM
Last modification on : Monday, February 10, 2020 - 6:14:09 PM
Contributor : Léna Le Roy <>
Submitted on : Thursday, July 5, 2018 - 11:29:16 AM
Last modification on : Monday, February 10, 2020 - 6:14:09 PM