Graph Signal Sampling via Reinforcement Learning
This work addresses graph signal sampling for applications like sensor networks, but it appears incremental as it applies a known algorithm to a specific domain.
The paper tackled the problem of sampling and recovering clustered graph signals by formulating it as a multi-armed bandit problem, and found that gradient MAB-based strategies outperformed existing methods in numerical experiments.
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.