Graph Signal Processing: Overview, Challenges, and Applications

被引：1183

作者：

Ortega, Antonio ^{[1
]}

Frossard, Pascal ^{[2
]}

Kovacevic, Jelena ^{[4
,5
,6
]}

Moura, Jose M. F. ^{[7
]}

Vandergheynst, Pierre ^{[3
]}

机构：

[1] Univ Southern Calif, Elect Engn Dept, Los Angeles, CA 90089 USA

[2] Ecole Polytech Fed Lausanne, Signal Proc Lab LTS4, CH-1015 Lausanne, Switzerland

[3] Ecole Polytech Fed Lausanne, Elect Engn, CH-1015 Lausanne, Switzerland

[4] Carnegie Mellon Univ, Elect & Comp Engn, Pittsburgh, PA 15213 USA

[5] Carnegie Mellon Univ, Biomed Engn, Pittsburgh, PA 15213 USA

[6] Carnegie Mellon Univ, Ctr Bioimage Informat, Pittsburgh, PA 15213 USA

[7] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA

来源：

PROCEEDINGS OF THE IEEE | 2018年 / 106卷 / 05期

关键词：

Graph signal processing (GSP); network science and graphs; sampling; signal processing; HUMAN BRAIN NETWORKS; HARMONIC-ANALYSIS; FILTER BANKS; DIMENSIONALITY REDUCTION; GEOMETRIC DIFFUSIONS; STRUCTURE DEFINITION; WAVELET FILTERBANKS; DISCRETE COSINE; EPIDEMICS; COMPRESSION;

D O I：

10.1109/JPROC.2018.2820126

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering, or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.

引用

页码：808 / 828

页数：21

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