Bayesian Compressive Sensing and Projection Optimization
Abstract
This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal of dimension N is measured accurately based on K N real measurements. This is achieved under the assumption that the underlying signal has a sparse representation in some basis (e.g., wavelets). In this paper we demonstrate how techniques developed in machine learning, specifically sparse Bayesian regression and active learning, may be leveraged to this new problem. We also point out future research directions in compressive sensing of interest to the machinelearning community.
Cite
Text
Ji and Carin. "Bayesian Compressive Sensing and Projection Optimization." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273544Markdown
[Ji and Carin. "Bayesian Compressive Sensing and Projection Optimization." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/ji2007icml-bayesian/) doi:10.1145/1273496.1273544BibTeX
@inproceedings{ji2007icml-bayesian,
title = {{Bayesian Compressive Sensing and Projection Optimization}},
author = {Ji, Shihao and Carin, Lawrence},
booktitle = {International Conference on Machine Learning},
year = {2007},
pages = {377-384},
doi = {10.1145/1273496.1273544},
url = {https://mlanthology.org/icml/2007/ji2007icml-bayesian/}
}