2020
DOI: 10.1016/j.sigpro.2019.107326
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Normalized LMS algorithm and data-selective strategies for adaptive graph signal estimation

Abstract: This work proposes a normalized least-mean-squares (NLMS) algorithm for online estimation of bandlimited graph signals (GS) using a reduced number of noisy measurements. As in the classical adaptive filtering framework, the resulting GS estimation technique converges faster than the least-mean-squares (LMS) algorithm while being less complex than the recursive least-squares (RLS) algorithm, both recently recast as adaptive estimation strategies for the GS framework. Detailed steady-state mean-squared error and… Show more

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Cited by 45 publications
(56 citation statements)
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“…The application of linear shift-invariant filter models is widely employed in the literature, e.g., to design graph spectral filters [11], [12] and model dynamic graph signals [15], [16]. Several works deal with adaptive learning of graph filters, see, e.g., [19]- [23]. These methods were later extended to multitask graphs [24], [25].…”
Section: Introductionmentioning
confidence: 99%
“…The application of linear shift-invariant filter models is widely employed in the literature, e.g., to design graph spectral filters [11], [12] and model dynamic graph signals [15], [16]. Several works deal with adaptive learning of graph filters, see, e.g., [19]- [23]. These methods were later extended to multitask graphs [24], [25].…”
Section: Introductionmentioning
confidence: 99%
“…Global methods, on the other hand, interpolate the unknown signal values at once and can provide better results by taking the entire network into account at the expensive of a higher computational burden [63], [65]. Many GSP interpolation schemes have been proposed [66]- [70].…”
Section: Introductionmentioning
confidence: 99%
“…Real networks and their corresponding data come in vastly different shapes and applications, ranging from genetic interaction networks [3] and the human brain [4] to sensor networks and smart cities [5]. Graph signal processing (GSP) explores pairwise relations between elements of a network to construct tools suitable for the processing of network data [1], [2], [6]- [12]. In GSP, networks are modeled as graphs and data defined over, or generated by, elements of these networks are modeled as a graph signal-a mapping from the set of vertices into the set of complex numbers.…”
Section: Introductionmentioning
confidence: 99%