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Signal stochastic decomposition over continuous dictionaries

Zacharie Naulet 1 Eric Barat 1
1 LM2S - Laboratoire Modélisation et Simulation de Systèmes
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
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.
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Submitted on : Thursday, July 5, 2018 - 11:29:16 AM
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Zacharie Naulet, Eric Barat. Signal stochastic decomposition over continuous dictionaries. 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Sep 2014, Reims, France. pp.6958857, ⟨10.1109/MLSP.2014.6958857⟩. ⟨cea-01830639⟩



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