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Reinforcement learning for graph signal processing

Graph Signal Sampling via Reinforcement Learning


We model the sampling and recovery of clustered graph signals as a reinforcement learning (RL) problem. The signal sampling is carried out by an agent which crawls over the graph and selects the most relevant graph nodes to sample. The goal of the agent is to select signal samples which allow for the most accurate recovery. The sample selection is formulated as a multi-armed bandit (MAB) problem, which lends naturally to learning efficient sampling strategies using the well-known gradient MAB algorithm.

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Authors:
Oleksii Abramenko, Alexander Jung
Submitted On:
30 May 2019 - 10:50am
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Poster_Abramenko_Jung.pdf

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[1] Oleksii Abramenko, Alexander Jung, "Graph Signal Sampling via Reinforcement Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4562. Accessed: Jun. 18, 2019.
@article{4562-19,
url = {http://sigport.org/4562},
author = {Oleksii Abramenko; Alexander Jung },
publisher = {IEEE SigPort},
title = {Graph Signal Sampling via Reinforcement Learning},
year = {2019} }
TY - EJOUR
T1 - Graph Signal Sampling via Reinforcement Learning
AU - Oleksii Abramenko; Alexander Jung
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4562
ER -
Oleksii Abramenko, Alexander Jung. (2019). Graph Signal Sampling via Reinforcement Learning. IEEE SigPort. http://sigport.org/4562
Oleksii Abramenko, Alexander Jung, 2019. Graph Signal Sampling via Reinforcement Learning. Available at: http://sigport.org/4562.
Oleksii Abramenko, Alexander Jung. (2019). "Graph Signal Sampling via Reinforcement Learning." Web.
1. Oleksii Abramenko, Alexander Jung. Graph Signal Sampling via Reinforcement Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4562