research-article

DealMVC: Dual Contrastive Calibration for Multi-view Clustering

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En ZhuAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 337 - 346

https://doi.org/10.1145/3581783.3611951

Published: 27 October 2023 Publication History

Abstract

Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe the following drawback, which limits the clustering performance from further improvement. The existing multi-view models mainly focus on the consistency of the same samples in different views while ignoring the circumstance of similar but different samples in cross-view scenarios. To solve this problem, we propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC). Specifically, we first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph. Moreover, to utilize the diversity of multi-view information, we propose a local contrastive calibration loss to constrain the consistency of pair-wise view features. The feature structure is regularized by reliable class information, thus guaranteeing similar samples have similar features in different views. During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels. In comparison with other state-of-the-art approaches, the comprehensive experimental results obtained from eight benchmark datasets provide substantial validation of the effectiveness and superiority of our algorithm. We release the code of DealMVC at https://github.com/xihongyang1999/DealMVC on GitHub.

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Cited By

Bai RHuang RXu LQin Y(2025)CSMDC: Exploring consistently context semantics for multi-view document clusteringExpert Systems with Applications10.1016/j.eswa.2024.125386261(125386)Online publication date: Feb-2025
https://doi.org/10.1016/j.eswa.2024.125386
Luo FLiu YGong XNan ZGuo TCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)EMVCC: Enhanced Multi-View Contrastive Clustering for Hyperspectral ImagesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681600(6288-6296)Online publication date: 28-Oct-2024
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Yuan HLai SLi XDai JSun YRen ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Robust Prototype Completion for Incomplete Multi-view ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681397(10402-10411)Online publication date: 28-Oct-2024
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Index Terms

DealMVC: Dual Contrastive Calibration for Multi-view Clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. 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 '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Cited By

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https://doi.org/10.1016/j.eswa.2024.125386
Luo FLiu YGong XNan ZGuo TCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)EMVCC: Enhanced Multi-View Contrastive Clustering for Hyperspectral ImagesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681600(6288-6296)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681600
Yuan HLai SLi XDai JSun YRen ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Robust Prototype Completion for Incomplete Multi-view ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681397(10402-10411)Online publication date: 28-Oct-2024
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He CZhu HHu PPeng XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Robust Variational Contrastive Learning for Partially View-unaligned ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681331(4167-4176)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681331
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Ma HWang SYu SLiu SHuang JWu HLiu XZhu ECai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Automatic and Aligned Anchor Learning Strategy for Multi-View ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681273(5045-5054)Online publication date: 28-Oct-2024
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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
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https://dl.acm.org/doi/10.1145/3664647.3680837
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