This work proposes distributed recursive least squares (RLS) strategies for adaptive reconstruction and learning of signals defined over graphs. First, we introduce a centralized RLS estimation strategy with probabilistic sampling, and we propose a sparse sensing method that selects the sampling probability at each node in the graph in order to guarantee adaptive signal reconstruction and a target steady-state performance. Then, a distributed RLS strategy is derived and is shown to be convergent to its centralized counterpart. The performed numerical tests show the performance of the proposed adaptive method for distributed learning of graph signals.

Distributed recursive least squares strategies for adaptive reconstruction of graph signals / Di Lorenzo, Paolo; Isufi, Elvin; Banelli, Paolo; Barbarossa, Sergio; Leus, Geert. - ELETTRONICO. - (2017), pp. 2289-2293. ((Intervento presentato al convegno EUSIPCO 2017 tenutosi a Kos, Grecia [10.23919/EUSIPCO.2017.8081618].

Distributed recursive least squares strategies for adaptive reconstruction of graph signals

Di Lorenzo, Paolo;BANELLI, PAOLO;Barbarossa, Sergio;
2017

Abstract

This work proposes distributed recursive least squares (RLS) strategies for adaptive reconstruction and learning of signals defined over graphs. First, we introduce a centralized RLS estimation strategy with probabilistic sampling, and we propose a sparse sensing method that selects the sampling probability at each node in the graph in order to guarantee adaptive signal reconstruction and a target steady-state performance. Then, a distributed RLS strategy is derived and is shown to be convergent to its centralized counterpart. The performed numerical tests show the performance of the proposed adaptive method for distributed learning of graph signals.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1064877
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