Sparse Signal Recovery Using Markov Random Fields
Abstract
Compressive Sensing (CS) combines sampling and compression into a single sub-Nyquist linear measurement process for sparse and compressible signals. In this paper, we extend the theory of CS to include signals that are concisely represented in terms of a graphical model. In particular, we use Markov Random Fields (MRFs) to represent sparse signals whose nonzero coefficients are clustered. Our new model-based reconstruction algorithm, dubbed Lattice Matching Pursuit (LaMP), stably recovers MRF-modeled signals using many fewer measurements and computations than the current state-of-the-art algorithms.
Cite
Text
Cevher et al. "Sparse Signal Recovery Using Markov Random Fields." Neural Information Processing Systems, 2008.Markdown
[Cevher et al. "Sparse Signal Recovery Using Markov Random Fields." Neural Information Processing Systems, 2008.](https://mlanthology.org/neurips/2008/cevher2008neurips-sparse/)BibTeX
@inproceedings{cevher2008neurips-sparse,
title = {{Sparse Signal Recovery Using Markov Random Fields}},
author = {Cevher, Volkan and Duarte, Marco F. and Hegde, Chinmay and Baraniuk, Richard},
booktitle = {Neural Information Processing Systems},
year = {2008},
pages = {257-264},
url = {https://mlanthology.org/neurips/2008/cevher2008neurips-sparse/}
}