Node Sampling by Partitioning on Graphs via Convex Optimization

Rusu, Cristian and Thompson, John (2017) Node Sampling by Partitioning on Graphs via Convex Optimization. In: 2017 Sensor Signal Processing for Defence Conference (SSPD). IEEE, London, pp. 1-5. ISBN 9781538616635

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Abstract

In this paper we deal with the problem of efficiently and accurately reconstructing a complete graph signal from partially observed noisy measurements. Given a graph structure, we propose a solution based on convex optimization techniques to partition the nodes of the graph into subsets such that sampling a graph signal from any of these subsets provides an accurate, low mean squared error for example, reconstruction of the original complete graph signal. We show how the proposed sampling set construction approach relates to optimal experimental design, sensor management, positioning and selection problems and provide numerical simulation results on synthetic and real-world graphs.

Item Type:	Book Section
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Caoimhe Ní Mhaicín
Date Deposited:	02 Jul 2018 10:08
Last Modified:	02 Jul 2018 10:08
URI:	https://norma.ncirl.ie/id/eprint/3027

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