Compressed Sensing for Block-Sparse Smooth Signals
Machine Learning
2013-09-11 v1 Information Theory
math.IT
Statistics Theory
Statistics Theory
Abstract
We present reconstruction algorithms for smooth signals with block sparsity from their compressed measurements. We tackle the issue of varying group size via group-sparse least absolute shrinkage selection operator (LASSO) as well as via latent group LASSO regularizations. We achieve smoothness in the signal via fusion. We develop low-complexity solvers for our proposed formulations through the alternating direction method of multipliers.
Cite
@article{arxiv.1309.2505,
title = {Compressed Sensing for Block-Sparse Smooth Signals},
author = {Shahzad Gishkori and Geert Leus},
journal= {arXiv preprint arXiv:1309.2505},
year = {2013}
}