The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a known bandwidth, the method enables reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of observations sampled over a subset of vertices. A detailed mean square analysis provides the performance of the proposed method, and leads to several insights for designing useful sampling strategies for graph signals. Numerical results validate our theoretical findings, and illustrate the advantages achieved by the proposed strategy for online estimation of band-limited graph signals. © 2016 IEEE.
LMS estimation of signals defined over graphs / Di Lorenzo, Paolo; Barbarossa, Sergio; Banelli, Paolo; Sardellitti, Stefania. - ELETTRONICO. - (2016), pp. 2121-2125. (Intervento presentato al convegno 24th European Signal Processing Conference, EUSIPCO 2016 tenutosi a Budapest; Hungary) [10.1109/EUSIPCO.2016.7760623].
LMS estimation of signals defined over graphs
Di Lorenzo, Paolo;BARBAROSSA, Sergio;SARDELLITTI, Stefania
2016
Abstract
The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a known bandwidth, the method enables reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of observations sampled over a subset of vertices. A detailed mean square analysis provides the performance of the proposed method, and leads to several insights for designing useful sampling strategies for graph signals. Numerical results validate our theoretical findings, and illustrate the advantages achieved by the proposed strategy for online estimation of band-limited graph signals. © 2016 IEEE.File | Dimensione | Formato | |
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