Sampling Theory for Graph Signals on Product Graphs
Information Theory
2018-09-27 v1 Artificial Intelligence
Machine Learning
math.IT
Abstract
In this paper, we extend the sampling theory on graphs by constructing a framework that exploits the structure in product graphs for efficient sampling and recovery of bandlimited graph signals that lie on them. Product graphs are graphs that are composed from smaller graph atoms; we motivate how this model is a flexible and useful way to model richer classes of data that can be multi-modal in nature. Previous works have established a sampling theory on graphs for bandlimited signals. Importantly, the framework achieves significant savings in both sample complexity and computational complexity
Cite
@article{arxiv.1809.10049,
title = {Sampling Theory for Graph Signals on Product Graphs},
author = {Rohan Varma and Jelena Kovačević},
journal= {arXiv preprint arXiv:1809.10049},
year = {2018}
}
Comments
Accepted to GlobalSIP 2018