Graph Signal Sampling via Reinforcement Learning
Machine Learning
2018-05-16 v1 Artificial Intelligence
Machine Learning
Abstract
We formulate the problem of sampling and recovering clustered graph signal as a multi-armed bandit (MAB) problem. This formulation lends naturally to learning sampling strategies using the well-known gradient MAB algorithm. In particular, the sampling strategy is represented as a probability distribution over the individual arms of the MAB and optimized using gradient ascent. Some illustrative numerical experiments indicate that the sampling strategies based on the gradient MAB algorithm outperform existing sampling methods.
Cite
@article{arxiv.1805.05827,
title = {Graph Signal Sampling via Reinforcement Learning},
author = {Oleksii Abramenko and Alexander Jung},
journal= {arXiv preprint arXiv:1805.05827},
year = {2018}
}