short-paper

Structural Deep Incomplete Multi-view Clustering Network

Authors:

Yong XuAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 3538 - 3542

https://doi.org/10.1145/3459637.3482192

Published: 30 October 2021 Publication History

Abstract

In recent years, incomplete multi-view clustering has drawn increasing attention due to the existence of large amounts of unlabeled incomplete data whose views are not fully observed in the practical applications. Although many traditional methods have been extended to address the incomplete learning problem, most of them exploit the shallow models and ignore the geometric structure. To address these issues, we proposed a structural deep incomplete multi-view clustering network. Specifically, the proposed method can simultaneously explore the high-level features and high-order geometric structure information of data with several view-specific graph convolutional encoder networks and can directly obtain the optimal clustering indicator matrix in one stage. Experimental results on several datasets with the comparison of state-of-the-art methods validate the superiority of the proposed method.

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Cited By

Dong ZJin JXiao YXiao BWang SLiu XZhu E(2025)Subgraph Propagation and Contrastive Calibration for Incomplete Multiview Data ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.335067136:2(3218-3230)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2024.3350671
Fu YLi YHuang QCui JWen J(2025)Anchor Graph Network for Incomplete Multiview ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.334940536:2(3708-3719)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2024.3349405
Chowdhury AGupta ADas S(2025)Deep multi-view clustering: A comprehensive survey of the contemporary techniquesInformation Fusion10.1016/j.inffus.2025.103012119(103012)Online publication date: Jul-2025
https://doi.org/10.1016/j.inffus.2025.103012
Show More Cited By

Index Terms

Structural Deep Incomplete Multi-view Clustering Network
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

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Publication History

Published: 30 October 2021

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Short-paper

Funding Sources

Shenzhen Fundamental Research Fund
Guangdong Basic and Applied Basic Research Foundation
National Natural Science Foundation of China
Establishment of Key Laboratory of Shenzhen Science and Technology Innovation Committee
Guangzhou Science and Technology Plan Project

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CIKM '21

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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Cited By

Dong ZJin JXiao YXiao BWang SLiu XZhu E(2025)Subgraph Propagation and Contrastive Calibration for Incomplete Multiview Data ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.335067136:2(3218-3230)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2024.3350671
Fu YLi YHuang QCui JWen J(2025)Anchor Graph Network for Incomplete Multiview ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.334940536:2(3708-3719)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2024.3349405
Chowdhury AGupta ADas S(2025)Deep multi-view clustering: A comprehensive survey of the contemporary techniquesInformation Fusion10.1016/j.inffus.2025.103012119(103012)Online publication date: Jul-2025
https://doi.org/10.1016/j.inffus.2025.103012
Wu YChen JHu LXu HLiang HWu J(2025)OmniFuse: A general modality fusion framework for multi-modality learning on low-quality medical dataInformation Fusion10.1016/j.inffus.2024.102890117(102890)Online publication date: May-2025
https://doi.org/10.1016/j.inffus.2024.102890
Yan XWang ZJin Y(2025)Federated Incomplete Multi-view Clustering with Heterogeneous Graph Neural NetworksFederated Learning in the Age of Foundation Models - FL 2024 International Workshops10.1007/978-3-031-82240-7_5(61-76)Online publication date: 4-Mar-2025
https://doi.org/10.1007/978-3-031-82240-7_5
Zhou LDu GLü KWang LDu J(2024)A Survey and an Empirical Evaluation of Multi-View Clustering ApproachesACM Computing Surveys10.1145/364510856:7(1-38)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3645108
Liu SLiu XWang SNiu XZhu E(2024)Fast Incomplete Multi-View Clustering With View-Independent AnchorsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.322048635:6(7740-7751)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3220486
Yu SZhang PWang SDong ZYang HZhu ELiu X(2024)Differentiated Anchor Quantity Assisted Incomplete Multiview Clustering Without Number-TuningIEEE Transactions on Cybernetics10.1109/TCYB.2024.344319854:11(7024-7037)Online publication date: Nov-2024
https://doi.org/10.1109/TCYB.2024.3443198
Huang BHuang ZLan SZheng QYu Y(2024)Incomplete Multi-View Clustering Via Inference and EvaluationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448378(8180-8184)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448378
Zhang KWang YGao SDu SHe X(2024)Direct Contrastive Learning for Incomplete Multi-view Clustering2024 36th Chinese Control and Decision Conference (CCDC)10.1109/CCDC62350.2024.10588205(6228-6233)Online publication date: 25-May-2024
https://doi.org/10.1109/CCDC62350.2024.10588205
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