Signal stochastic decomposition over continuous dictionaries

Author

Naulet, Zacharie ; Barat, E.

Author_Institution

Lab. of Modeling, Simulation & Syst., CEA, Gif-sur-Yvette, France

fYear

2014

fDate

21-24 Sept. 2014

Firstpage

1

Lastpage

6

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

Bayes methods; inverse problems; nonparametric statistics; regression analysis; signal processing; smoothing methods; stochastic processes; Bayesian nonparametrics method; approximate sparsity; continuous dictionaries; coorbit theory; ill-posed inverse problem; indirect noisy observations; posterior computation; quantum homodyne tomography; signal stochastic decomposition; smoothness properties; sparse regression problem; Abstracts; Atomic measurements; Bayes methods; Dictionaries; Time-frequency analysis; Wavelet transforms; Bayesian nonparametrics; Coorbit Theory; Sparse regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on

Conference_Location

Reims

Type

conf

DOI

10.1109/MLSP.2014.6958857

Filename

6958857