The goal of this paper is to propose adaptive strategies for distributed learning of signals defined over graphs. Assuming the graph signal to be band-limited, the method enables distributed adaptive reconstruction from a limited number of sampled observations taken from a subset of vertices. A detailed mean square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, a distributed selection strategy for the sampling set is provided. Several numerical results validate our methodology, and illustrate the performance of the proposed algorithm for distributed adaptive learning of graph signals. © 2016 IEEE.

Distributed adaptive learning of signals defined over graphs

SARDELLITTI, Stefania
2016-01-01

Abstract

The goal of this paper is to propose adaptive strategies for distributed learning of signals defined over graphs. Assuming the graph signal to be band-limited, the method enables distributed adaptive reconstruction from a limited number of sampled observations taken from a subset of vertices. A detailed mean square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, a distributed selection strategy for the sampling set is provided. Several numerical results validate our methodology, and illustrate the performance of the proposed algorithm for distributed adaptive learning of graph signals. © 2016 IEEE.
2016
9781538639542
adaptation and learning over networks
distributed estimation
graph signal processing
sampling on graphs
signal processing
computer networks and communications
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12606/7683
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