A survey of signal processing based graph learning techniques

被引：0

作者：

Subbareddy, B. ^{[1
]}

Reddy, P. Charantej ^{[1
]}

Siripuram, Aditya ^{[1
]}

Zhang, Jingxin ^{[2
]}

机构：

[1] Indian Inst Technol Hyderabad, Hyderabad, India

[2] Swinburne Univ Technol, Melbourne, Vic, Australia

来源：

2019 1ST INTERNATIONAL CONFERENCE ON INDUSTRIAL ARTIFICIAL INTELLIGENCE (IAI 2019) | 2019年

关键词：

Graph signal processing; graph learning; network topology inference; sparse signal processing; MODEL SELECTION; INFERENCE; SPARSE;

D O I：

10.1109/iciai.2019.8850827

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recent advances in technology have led to easy data acquisition mechanisms in many fields, leading to massive datasets. It is often of interest to understand the inherent structure and learn the best representation of the given dataset. Graphs are a powerful way to model interrelationships between data features - well constructed meaningful graphs help in representing and processing the data effectively. The graph topology needs to be inferred from the observed data. In this survey, we briefly explore signal processing based graph learning approaches that have been proposed in the literature and propose new research directions.

引用

页数：6

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