research-article

Multi-view Self-Expressive Subspace Clustering Network

Authors:

Jie WenAuthors Info & Claims

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

Pages 417 - 425

https://doi.org/10.1145/3581783.3612237

Published: 27 October 2023 Publication History

Abstract

Advanced deep multi-view subspace clustering methods are based on the self-expressive model, which has achieved impressive performance. However, most existing works have several limitations: 1) They endure high computational complexity when learning a consistent affinity matrix, impeding their capacity to handle large-scale multi-view data; 2) The global and local structure information of multi-view data remains under-explored. To tackle these challenges, we propose a simplistic but comprehensive framework called Multi-view Self-Expressive Subspace Clustering (MSESC) network. Specifically, we design a deep metric network to replace the conventional self-expressive model, which can directly and efficiently produce the intrinsic similarity values of any instance-pairs of all views. Moreover, our method explores global and local structure information from the connectivity of instance-pairs across views and the nearest neighbors of instance-pairs within the view, respectively. By integrating global and local structure information within a unified framework, MSESC can learn a high-quality shared affinity matrix for better clustering performance. Extensive experimental results indicate the superiority of MSESC compared to several state-of-the-art methods.

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Cited By

Lin YLiu HWang RGuo QZhang C(2025)Multiview Feature Decoupling for Deep Subspace ClusteringIEEE Transactions on Multimedia10.1109/TMM.2024.352177627(544-556)Online publication date: 2025
https://doi.org/10.1109/TMM.2024.3521776
Shu ZSun TYu Z(2025)Self-supervised disentangled representation learning with distribution alignment for multi-view clusteringDigital Signal Processing10.1016/j.dsp.2025.105078(105078)Online publication date: Feb-2025
https://doi.org/10.1016/j.dsp.2025.105078
Zhang CLiang XZhou PLing ZZhang YWu XSheng WJiang BCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Scalable Multi-view Unsupervised Feature Selection with Structure Learning and FusionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681223(5479-5488)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681223

Index Terms

Multi-view Self-Expressive Subspace 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

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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Shenzhen Higher Education Stability Support Program Project
Guangdong Basic and Applied Basic Research Foundation
National Natural Science Foundation of China

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MM '23

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SIGMM

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

October 29 - November 3, 2023

Ottawa ON, Canada

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

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Cited By

Lin YLiu HWang RGuo QZhang C(2025)Multiview Feature Decoupling for Deep Subspace ClusteringIEEE Transactions on Multimedia10.1109/TMM.2024.352177627(544-556)Online publication date: 2025
https://doi.org/10.1109/TMM.2024.3521776
Shu ZSun TYu Z(2025)Self-supervised disentangled representation learning with distribution alignment for multi-view clusteringDigital Signal Processing10.1016/j.dsp.2025.105078(105078)Online publication date: Feb-2025
https://doi.org/10.1016/j.dsp.2025.105078
Zhang CLiang XZhou PLing ZZhang YWu XSheng WJiang BCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Scalable Multi-view Unsupervised Feature Selection with Structure Learning and FusionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681223(5479-5488)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681223

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