Graph Signal Sampling via Reinforcement Learning

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A4 Artikkeli konferenssijulkaisussa
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Date

2019-05-01

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Department of Computer Science

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en

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5

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44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings, pp. 3077-3081, IEEE International Conference on Acoustics Speech and Signal Processing

Abstract

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. In a nutshell, 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 obtained from the gradient MAB algorithm outperform existing sampling methods.

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Keywords

machine learning, reinforcement learning, multi-armed bandit, graph signal processing, total variation

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DOI

10.1109/ICASSP.2019.8683181

Citation

Abramenko, O & Jung, A 2019, Graph Signal Sampling via Reinforcement Learning . in 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings ., 8683181, IEEE International Conference on Acoustics Speech and Signal Processing, IEEE, pp. 3077-3081, IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 12/05/2019 . https://doi.org/10.1109/ICASSP.2019.8683181

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https://urn.fi/URN:NBN:fi:aalto-201906203857

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[article-cris] Perustieteiden korkeakoulu / SCI