publication . Article . Preprint . 2017

autoregressive moving average graph filtering

Isufi, Elvin; Loukas, Andreas; Simonetto, Andrea; Leus, Geert;
Open Access
  • Published: 15 Jan 2017 Journal: IEEE Transactions on Signal Processing, volume 65, pages 274-288 (issn: 1053-587X, eissn: 1941-0476, Copyright policy)
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogs of classical filters, but intended for signals defined on graphs. This paper brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which are able to approximate any desired graph frequency response, and give exact solutions for specific graph signal denoising and interpolation problems. The philosophy to design the ARMA coefficients independently from the underlying graph renders the ARMA graph filters suitable in static and, particularly, time-varying settings. The latter occ...
ACM Computing Classification System: MathematicsofComputing_DISCRETEMATHEMATICS
free text keywords: Signal Processing, Electrical and Electronic Engineering, Mathematics, Voltage graph, Integral graph, Null graph, Quartic graph, Strength of a graph, Graph bandwidth, Mathematical optimization, Graph power, Topological graph theory, Computer Science - Learning, Computer Science - Systems and Control, Statistics - Machine Learning
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publication . Article . Preprint . 2017

autoregressive moving average graph filtering

Isufi, Elvin; Loukas, Andreas; Simonetto, Andrea; Leus, Geert;