publication . Preprint . Article . 2016

Autoregressive Moving Average Graph Filtering

Elvin Isufi; Andreas Loukas; Andrea Simonetto; Geert Leus;
Open Access English
  • Published: 14 Feb 2016
Abstract
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...
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Subjects
ACM Computing Classification System: MathematicsofComputing_DISCRETEMATHEMATICS
free text keywords: Computer Science - Learning, Computer Science - Systems and Control, Statistics - Machine Learning, Signal Processing, Electrical and Electronic Engineering, Strength of a graph, Discrete mathematics, Integral graph, Voltage graph, Mathematics, Graph bandwidth, Algorithm, Null graph, Graph power, Quartic graph, Topological graph theory
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