Separable autoregressive moving average graph-temporal filters

Elvin Isufi; Andreas Loukas; Andrea Simonetto; Geert Leus

doi:10.1109/EUSIPCO.2016.7760238

Separable autoregressive moving average graph-temporal filters

Elvin Isufi
, Andreas Loukas
, Andrea Simonetto
, Geert Leus

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

28 Scopus citations

Abstract

Despite their widespread use for the analysis of graph data, current graph filters are designed for graph signals that do not change over time, and thus they cannot simultaneously process time and graph frequency content in an adequate manner. This work presents ARMA^2D, an autoregressive moving average graph-temporal filter that captures jointly the signal variations over the graph and time. By its unique nature, this filter is able to achieve a separable 2-dimensional frequency response, making it possible to approximate the filtering specifications along both the graph and temporal frequency domains. Numerical results show that the proposed solution outperforms the state of the art graph filters when the graph signal is timevarying.

Original language	English
Title of host publication	2016 24th European Signal Processing Conference, EUSIPCO 2016
Publisher	European Signal Processing Conference, EUSIPCO
Pages	200-204
Number of pages	5
ISBN (Electronic)	9780992862657
DOIs	https://doi.org/10.1109/EUSIPCO.2016.7760238
State	Published - 28 Nov 2016
Externally published	Yes
Event	24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary Duration: 28 Aug 2016 → 2 Sep 2016

Publication series

Name	European Signal Processing Conference
Volume	2016-November
ISSN (Print)	2219-5491

Conference

Conference	24th European Signal Processing Conference, EUSIPCO 2016
Country/Territory	Hungary
City	Budapest
Period	28/08/16 → 2/09/16

Keywords

Distributed signal processing
Graph filters
Separable graph-temporal filters
Signal processing over graphs

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/EUSIPCO.2016.7760238

Cite this

Isufi, E., Loukas, A., Simonetto, A., & Leus, G. (2016). Separable autoregressive moving average graph-temporal filters. In 2016 24th European Signal Processing Conference, EUSIPCO 2016 (pp. 200-204). Article 7760238 (European Signal Processing Conference; Vol. 2016-November). European Signal Processing Conference, EUSIPCO. https://doi.org/10.1109/EUSIPCO.2016.7760238

@inproceedings{261dc4e919354b7496bf983c7f261283,

title = "Separable autoregressive moving average graph-temporal filters",

abstract = "Despite their widespread use for the analysis of graph data, current graph filters are designed for graph signals that do not change over time, and thus they cannot simultaneously process time and graph frequency content in an adequate manner. This work presents ARMA2D, an autoregressive moving average graph-temporal filter that captures jointly the signal variations over the graph and time. By its unique nature, this filter is able to achieve a separable 2-dimensional frequency response, making it possible to approximate the filtering specifications along both the graph and temporal frequency domains. Numerical results show that the proposed solution outperforms the state of the art graph filters when the graph signal is timevarying.",

keywords = "Distributed signal processing, Graph filters, Separable graph-temporal filters, Signal processing over graphs",

author = "Elvin Isufi and Andreas Loukas and Andrea Simonetto and Geert Leus",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 24th European Signal Processing Conference, EUSIPCO 2016 ; Conference date: 28-08-2016 Through 02-09-2016",

year = "2016",

month = nov,

day = "28",

doi = "10.1109/EUSIPCO.2016.7760238",

language = "English",

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Isufi, E, Loukas, A, Simonetto, A & Leus, G 2016, Separable autoregressive moving average graph-temporal filters. in 2016 24th European Signal Processing Conference, EUSIPCO 2016., 7760238, European Signal Processing Conference, vol. 2016-November, European Signal Processing Conference, EUSIPCO, pp. 200-204, 24th European Signal Processing Conference, EUSIPCO 2016, Budapest, Hungary, 28/08/16. https://doi.org/10.1109/EUSIPCO.2016.7760238

Separable autoregressive moving average graph-temporal filters. / Isufi, Elvin; Loukas, Andreas; Simonetto, Andrea et al.
2016 24th European Signal Processing Conference, EUSIPCO 2016. European Signal Processing Conference, EUSIPCO, 2016. p. 200-204 7760238 (European Signal Processing Conference; Vol. 2016-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Separable autoregressive moving average graph-temporal filters

AU - Isufi, Elvin

AU - Loukas, Andreas

AU - Simonetto, Andrea

AU - Leus, Geert

PY - 2016/11/28

Y1 - 2016/11/28

N2 - Despite their widespread use for the analysis of graph data, current graph filters are designed for graph signals that do not change over time, and thus they cannot simultaneously process time and graph frequency content in an adequate manner. This work presents ARMA2D, an autoregressive moving average graph-temporal filter that captures jointly the signal variations over the graph and time. By its unique nature, this filter is able to achieve a separable 2-dimensional frequency response, making it possible to approximate the filtering specifications along both the graph and temporal frequency domains. Numerical results show that the proposed solution outperforms the state of the art graph filters when the graph signal is timevarying.

AB - Despite their widespread use for the analysis of graph data, current graph filters are designed for graph signals that do not change over time, and thus they cannot simultaneously process time and graph frequency content in an adequate manner. This work presents ARMA2D, an autoregressive moving average graph-temporal filter that captures jointly the signal variations over the graph and time. By its unique nature, this filter is able to achieve a separable 2-dimensional frequency response, making it possible to approximate the filtering specifications along both the graph and temporal frequency domains. Numerical results show that the proposed solution outperforms the state of the art graph filters when the graph signal is timevarying.

KW - Distributed signal processing

KW - Graph filters

KW - Separable graph-temporal filters

KW - Signal processing over graphs

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U2 - 10.1109/EUSIPCO.2016.7760238

DO - 10.1109/EUSIPCO.2016.7760238

M3 - Conference contribution

AN - SCOPUS:85005994493

T3 - European Signal Processing Conference

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BT - 2016 24th European Signal Processing Conference, EUSIPCO 2016

PB - European Signal Processing Conference, EUSIPCO

T2 - 24th European Signal Processing Conference, EUSIPCO 2016

Y2 - 28 August 2016 through 2 September 2016

ER -

Separable autoregressive moving average graph-temporal filters

Abstract

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Keywords

ASJC Scopus subject areas

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