research-article

DIMC-net: Deep Incomplete Multi-view Clustering Network

Authors:

Bob ZhangAuthors Info & Claims

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

Pages 3753 - 3761

https://doi.org/10.1145/3394171.3413807

Published: 12 October 2020 Publication History

Abstract

In this paper, a new deep incomplete multi-view clustering network, called DIMC-net, is proposed to address the challenge of multi-view clustering on missing views. In particular, DIMC-net designs several view-specific encoders to extract the high-level information of multiple views and introduces a fusion graph based constraint to explore the local geometric information of data. To reduce the negative influence of missing views, a weighted fusion layer is introduced to obtain the consensus representation shared by all views. Moreover, a clustering layer is introduced to guarantee that the obtained consensus representation is the best one for the clustering task. Compared with the existing deep learning based approaches, DIMC-net is more flexible and efficient since it can handle all kinds of incomplete cases and directly produce the clustering results. Experimental results show that DIMC-net achieves significant improvement over state-of-the-art incomplete multi-view clustering methods.

Supplementary Material

MP4 File (3394171.3413807.mp4)

In this video, we briefly introduce our proposed DIMC-net (Deep incomplete multi-view clustering network) for the challenging multi-view clustering problem on incomplete views.

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8.93 MB

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Cited By

Sun SQi YXu J(2025)A robust multiview fuzzy clustering method combining local spatial informationInternational Conference on Mechatronics and Intelligent Control (ICMIC 2024)10.1117/12.3045530(122)Online publication date: 16-Jan-2025
https://doi.org/10.1117/12.3045530
Xu XWang ZRen SNiu SLi D(2025)Local–Global Geometric Information and View Complementarity Introduced Multiview Metric LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.338002036:3(5428-5441)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3380020
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
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Index Terms

DIMC-net: 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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Published In

cover image ACM Conferences

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

October 2020

4889 pages

ISBN:9781450379885

DOI:10.1145/3394171

General Chairs:
Chang Wen Chen
Chinese University of Hong Kong, Shenzhen, China
,
Rita Cucchiara
UNIMORE, Italy
,
Xian-Sheng Hua
Alibaba Group, China
,
Program Chairs:
Guo-Jun Qi
Futurewei Technologies, USA
,
Elisa Ricci
UNITN & Fondazione Bruno Kessler, Italy
,
Zhengyou Zhang
Tencent, China
,
Roger Zimmermann
National University of Singapore, Singapore

Copyright © 2020 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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New York, NY, United States

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Published: 12 October 2020

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University of Macau
Establishment of Key Laboratory of Shenzhen Science and Technology Innovation Committee
Guangdong Basic and Applied Basic Research Foundation

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MM '20

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SIGMM

MM '20: The 28th ACM International Conference on Multimedia

October 12 - 16, 2020

WA, Seattle, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Sun SQi YXu J(2025)A robust multiview fuzzy clustering method combining local spatial informationInternational Conference on Mechatronics and Intelligent Control (ICMIC 2024)10.1117/12.3045530(122)Online publication date: 16-Jan-2025
https://doi.org/10.1117/12.3045530
Xu XWang ZRen SNiu SLi D(2025)Local–Global Geometric Information and View Complementarity Introduced Multiview Metric LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.338002036:3(5428-5441)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3380020
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
Xu ZLang CWei LLiang LWang TLi YKampffmeyer M(2025)UNAGI: Unified neighbor-aware graph neural network for multi-view clusteringNeural Networks10.1016/j.neunet.2025.107193185(107193)Online publication date: May-2025
https://doi.org/10.1016/j.neunet.2025.107193
Wang ZDu YWang YNing RLi L(2025)Deep Incomplete Multi-view Clustering via Multi-level Imputation and Contrastive AlignmentNeural Networks10.1016/j.neunet.2024.106851181(106851)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106851
Zhu ZPu CZhang XWang JZhou X(2025)Dual-dimensional contrastive learning for incomplete multi-view clusteringNeurocomputing10.1016/j.neucom.2024.128892615(128892)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2024.128892
Wang HZhang WWang QMa X(2025)Adaptive structural-guided multi-level representation learning with graph contrastive for incomplete multi-view clusteringInformation Fusion10.1016/j.inffus.2025.103035119(103035)Online publication date: Jul-2025
https://doi.org/10.1016/j.inffus.2025.103035
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
Chen ZWu XXu TLi HKittler J(2025)Multi-layer multi-level comprehensive learning for deep multi-view clusteringInformation Fusion10.1016/j.inffus.2024.102785116(102785)Online publication date: Apr-2025
https://doi.org/10.1016/j.inffus.2024.102785
Song PMu JCheng YLiu ZZheng W(2025)Essential anchor graph learning for incomplete multi-view clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2025.110194145(110194)Online publication date: Apr-2025
https://doi.org/10.1016/j.engappai.2025.110194
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