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Conference Papers Year : 2014

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.
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Dates and versions

cea-01830639 , version 1 (05-07-2018)

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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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