Autoregressive Moving Average Graph Filtering

  title={Autoregressive Moving Average Graph Filtering},
  author={Elvin Isufi and Andreas Loukas and Andrea Simonetto and Geert Leus},
  journal={IEEE Transactions on Signal Processing},
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… CONTINUE READING
Highly Cited
This paper has 51 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 38 extracted citations

51 Citations

Citations per Year
Semantic Scholar estimates that this publication has 51 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 29 references

Statistical Digital Signal Processing and Modeling

  • M. H. Hayes
  • Hoboken, NJ, USA: Wiley,
  • 2009
Highly Influential
5 Excerpts

Similar Papers

Loading similar papers…