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 / Di Lorenzo, Paolo; Banelli, Paolo; BARBAROSSA, Sergio; SARDELLITTI, Stefania. - ELETTRONICO. - (2016), pp. 527-531. ((Intervento presentato al convegno 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 tenutosi a Asilomar Hotel and Conference Grounds, USA [10.1109/ACSSC.2016.7869096].

Distributed adaptive learning of signals defined over graphs

Di Lorenzo, Paolo;BARBAROSSA, Sergio;SARDELLITTI, Stefania
2016

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/958784
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