@inproceedings{f73b66aa4cab4add9123e1c30884f82c,
title = "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 = "Bayesian nonparametrics, Coorbit Theory, Sparse regression",
author = "Zacharie Naulet and {\'E}ric Barat",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014 ; Conference date: 21-09-2014 Through 24-09-2014",
year = "2014",
month = nov,
day = "14",
doi = "10.1109/MLSP.2014.6958857",
language = "English",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Mamadou Mboup and Tulay Adali and Eric Moreau and Jan Larsen",
booktitle = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
}