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Robust Diversified Graph Contrastive Network for Incomplete Multi-view Clustering

Authors:

Zhongchao Guan,

Meiyu LiangAuthors Info & Claims

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

Pages 3936 - 3944

https://doi.org/10.1145/3503161.3547894

Published: 10 October 2022 Publication History

Abstract

Incomplete multi-view clustering is a challenging task which aims to partition the unlabeled incomplete multi-view data into several clusters. The existing incomplete multi-view clustering methods neglect to utilize the diversified correlations inherent in data and handle the noise contained in different views. To address these issues, we propose a Robust Diversified Graph Contrastive Network (RDGC) for incomplete multi-view clustering, which integrates multi-view representation learning and diversified graph contrastive regularization into a unified framework. Multi-view unified and specific encoding network is developed to fuse different views into a unified representation, which can flexibly estimate the importance of views for incomplete multi-view data. Robust diversified graph contrastive regularization is proposed which captures the diversified data correlations to improve the discriminating power of the learned representation and reduce the information loss caused by the view missing problem. Moreover, our method can effectively resist the influence of noise and unreliable views by leveraging the robust contrastive learning loss. Extensive experiments conducted on four multi-view clustering datasets demonstrate the superiority of our method over the state-of-the-art methods.

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Cited By

Zou GYe YChen THu SCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Learning Dual Enhanced Representation for Contrastive Multi-view ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681030(8731-8739)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681030
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
Wu SZheng YRen YHe JPu XHuang SHao ZHe L(2024)Self-Weighted Contrastive Fusion for Deep Multi-View ClusteringIEEE Transactions on Multimedia10.1109/TMM.2024.338729826(9150-9162)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3387298
Show More Cited By

Index Terms

Robust Diversified Graph Contrastive Network for Incomplete Multi-view Clustering
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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cover image ACM Conferences

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

October 2022

7537 pages

ISBN:9781450392037

DOI:10.1145/3503161

General Chairs:
João Magalhães
NOVA University of Lisbon, Portugal
,
Alberto del Bimbo
University of Florence, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Xavier Alameda-Pineda
Inria, Grenoble, France
,
Qin Jin
Renmin University of China, China
,
Vincent Oria
New Jersey Institute of Technology, USA
,
Laura Toni
University College London, UK

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Published: 10 October 2022

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National Natural Science Foundation of China
CAAI-Huawei MindSpore Open Fund

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MM '22

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MM '22: The 30th ACM International Conference on Multimedia

October 10 - 14, 2022

Lisboa, Portugal

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Cited By

Zou GYe YChen THu SCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Learning Dual Enhanced Representation for Contrastive Multi-view ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681030(8731-8739)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681030
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
Wu SZheng YRen YHe JPu XHuang SHao ZHe L(2024)Self-Weighted Contrastive Fusion for Deep Multi-View ClusteringIEEE Transactions on Multimedia10.1109/TMM.2024.338729826(9150-9162)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3387298
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
Li ALi YXue ZGuan ZZhuang M(2024)Learning Hierarchy-Aware Federated Graph Embedding for Link Prediction2024 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp60711.2024.00059(329-336)Online publication date: 18-Feb-2024
https://doi.org/10.1109/BigComp60711.2024.00059
Bai SZheng QRen XZhu J(2024)Graph-guided imputation-free incomplete multi-view clusteringExpert Systems with Applications10.1016/j.eswa.2024.125165(125165)Online publication date: Aug-2024
https://doi.org/10.1016/j.eswa.2024.125165
Xiao SLi JLu JHuang SZeng BWang S(2024)Graph neural networks for multi-view learning: a taxonomic reviewArtificial Intelligence Review10.1007/s10462-024-10990-157:12Online publication date: 21-Oct-2024
https://doi.org/10.1007/s10462-024-10990-1
Gu SYuan XZhao LLiu ZHu YChen ZEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)MVCIR-net: Multi-view Clustering Information Reinforcement NetworkProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613833(3609-3618)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613833
Yang LWang JZhang THe DWang CGuo YCao XNiu BWang ZEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Long Short-Term Graph Memory Against Class-imbalanced Over-smoothingProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612566(2955-2963)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612566
Zhou HXue ZLiu YLi BDu JLiang MQi YEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)CALM: An Enhanced Encoding and Confidence Evaluating Framework for Trustworthy Multi-view LearningProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611965(3108-3116)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611965
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