• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
成果搜索

author:

Xiao, Shunxin (Xiao, Shunxin.) [1] | Li, Jiacheng (Li, Jiacheng.) [2] | Lu, Jielong (Lu, Jielong.) [3] | Huang, Sujia (Huang, Sujia.) [4] | Zeng, Bao (Zeng, Bao.) [5] | Wang, Shiping (Wang, Shiping.) [6] (Scholars:王石平)

Indexed by:

EI Scopus SCIE

Abstract:

With the explosive growth of user-generated content, multi-view learning has become a rapidly growing direction in pattern recognition and data analysis areas. Due to the significant application value of multi-view learning, there has been a continuous emergence of research based on machine learning methods and traditional deep learning paradigms. The core challenge in multi-view learning lies in harnessing both consistent and complementary information to forge a unified, comprehensive representation. However, many multi-view learning tasks are based on graph-structured data, making existing methods unable to effectively mine the information contained in the input multiple data sources. Recently, graph neural networks (GNN) techniques have been widely utilized to deal with non-Euclidean data, such as graphs or manifolds. Thus, it is essential to combine the advantages of the powerful learning capability of GNN models and multi-view data. In this paper, we aim to provide a comprehensive survey of recent research works on GNN-based multi-view learning. In detail, we first provide a taxonomy of GNN-based multi-view learning methods according to the input form of models: multi-relation, multi-attribute and mixed. Then, we introduce the applications of multi-view learning, including recommendation systems, computer vision and so on. Moreover, several public datasets and open-source codes are introduced for implementation. Finally, we analyze the challenges of applying GNN models on various multi-view learning tasks and state new future directions in this field.

Keyword:

Deep learning Graph neural network Multi-view learning Survey

Community:

  • [ 1 ] [Xiao, Shunxin]Xiamen Univ Technol, Sch Comp & Informat Engn, Xiamen 361024, Peoples R China
  • [ 2 ] [Zeng, Bao]Xiamen Univ Technol, Sch Comp & Informat Engn, Xiamen 361024, Peoples R China
  • [ 3 ] [Li, Jiacheng]Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350116, Peoples R China
  • [ 4 ] [Lu, Jielong]Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350116, Peoples R China
  • [ 5 ] [Huang, Sujia]Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350116, Peoples R China
  • [ 6 ] [Wang, Shiping]Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350116, Peoples R China

Reprint 's Address:

  • [Wang, Shiping]Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350116, Peoples R China;;

Show more details

Related Keywords:

Source :

ARTIFICIAL INTELLIGENCE REVIEW

ISSN: 0269-2821

Year: 2024

Issue: 12

Volume: 57

1 0 . 7 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

Online/Total:67/10011816
Address:FZU Library(No.2 Xuyuan Road, Fuzhou, Fujian, PRC Post Code:350116) Contact Us:0591-22865326
Copyright:FZU Library Technical Support:Beijing Aegean Software Co., Ltd. 闽ICP备05005463号-1