Multi-Task Compressive Sensing with Dirichlet Process Priors
Abstract
Compressive sensing (CS) is an emerging field that, under appropriate conditions, can significantly reduce the number of measurements required for a given signal. In many applications, one is interested in multiple signals that may be measured in multiple CS-type measurements, where here each signal corresponds to a sensing "task". In this paper we propose a novel multi-task compressive sensing framework based on a Bayesian formalism, where a Dirichlet process (DP) prior is employed, yielding a principled means of simultaneously inferring the appropriate sharing mechanisms as well as CS inversion for each task. A variational Bayesian (VB) inference algorithm is employed to estimate the full posterior on the model parameters.
Cite
Text
Qi et al. "Multi-Task Compressive Sensing with Dirichlet Process Priors." International Conference on Machine Learning, 2008. doi:10.1145/1390156.1390253Markdown
[Qi et al. "Multi-Task Compressive Sensing with Dirichlet Process Priors." International Conference on Machine Learning, 2008.](https://mlanthology.org/icml/2008/qi2008icml-multi/) doi:10.1145/1390156.1390253BibTeX
@inproceedings{qi2008icml-multi,
title = {{Multi-Task Compressive Sensing with Dirichlet Process Priors}},
author = {Qi, Yuting and Liu, Dehong and Dunson, David B. and Carin, Lawrence},
booktitle = {International Conference on Machine Learning},
year = {2008},
pages = {768-775},
doi = {10.1145/1390156.1390253},
url = {https://mlanthology.org/icml/2008/qi2008icml-multi/}
}