Print Email Facebook Twitter Adaptive Graph Signal Processing Title Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies Author Di Lorenzo, Paolo (Sapienza University of Rome) Banelli, Paolo (University of Perugia) Isufi, E. (TU Delft Signal Processing Systems; University of Perugia) Barbarossa, Sergio (Sapienza University of Rome) Leus, G.J.T. (TU Delft Signal Processing Systems) Date 2018 Abstract The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly) time-varying subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely, the least mean squares (LMS) and the recursive least squares (RLS) adaptive estimation strategies. For both methods, a detailed mean-square analysis illustrates the effect of random sampling on the adaptive reconstruction capability and the steady-state performance. Then, several probabilistic sampling strategies are proposed to design the sampling probability at each node in the graph, with the aim of optimizing the tradeoff between steady-state performance, graph sampling rate, and convergence rate of the adaptive algorithms. Finally, a distributed RLS strategy is derived and is shown to be convergent to its centralized counterpart. Numerical simulations carried out over both synthetic and real data illustrate the good performance of the proposed sampling and reconstruction strategies for (possibly distributed) adaptive learning of signals defined over graphs. Subject Adaptation and learningAdaptive learninggraph signal processingLaplace equationssampling on graphsSignal processingSignal processing algorithmsSteady-statesuccessive convex approximationTask analysisTools To reference this document use: http://resolver.tudelft.nl/uuid:ff88f949-3417-467e-a1de-cc5af126982a DOI 10.1109/TSP.2018.2835384 Embargo date 2019-01-01 ISSN 1053-587X Source IEEE Transactions on Signal Processing, 66 (13), 3584-3598 Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2018 Paolo Di Lorenzo, Paolo Banelli, E. Isufi, Sergio Barbarossa, G.J.T. Leus Files PDF Adaptive_Graph_Signal_Pro ... tegies.pdf 1.38 MB Close viewer /islandora/object/uuid:ff88f949-3417-467e-a1de-cc5af126982a/datastream/OBJ/view