Loading…

Geometric machine learning: research and applications

Over the last decade, deep learning has revolutionized many traditional machine learning tasks, ranging from computer vision to natural language processing. Although deep learning has achieved excellent performance, it does not perform as well as expected on geometric (non-Euclidean domain) data. Re...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications 2022-09, Vol.81 (21), p.30545-30597
Main Authors: Cao, Wenming, Zheng, Canta, Yan, Zhiyue, He, Zhihai, Xie, Weixin
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Over the last decade, deep learning has revolutionized many traditional machine learning tasks, ranging from computer vision to natural language processing. Although deep learning has achieved excellent performance, it does not perform as well as expected on geometric (non-Euclidean domain) data. Recently, many studies on extending deep learning approaches for graphs and manifolds have merged. In this article, we aim to provide a comprehensive overview of geometric deep learning and comparative methods. First, we introduce the related work and history of the geometric deep learning field and the theoretical background. Next, we summarize the evaluation of the methods of graph and manifold. We further discuss the applications and benchmark datasets of these methods across various research domains. Finally, we propose potential research directions and challenges in this rapidly growing field.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-022-12683-9