research-article

Scalable Multi-view Subspace Clustering with Unified Anchors

Authors:

Changjian WangAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 3528 - 3536

https://doi.org/10.1145/3474085.3475516

Published: 17 October 2021 Publication History

Abstract

Multi-view subspace clustering has received widespread attention to effectively fuse multi-view information among multimedia applications. Considering that most existing approaches' cubic time complexity makes it challenging to apply to realistic large-scale scenarios, some researchers have addressed this challenge by sampling anchor points to capture distributions in different views. However, the separation of the heuristic sampling and clustering process leads to weak discriminate anchor points. Moreover, the complementary multi-view information has not been well utilized since the graphs are constructed independently by the anchors from the corresponding views. To address these issues, we propose a Scalable Multi-view Subspace Clustering with Unified Anchors (SMVSC). To be specific, we combine anchor learning and graph construction into a unified optimization framework. Therefore, the learned anchors can represent the actual latent data distribution more accurately, leading to a more discriminative clustering structure. Most importantly, the linear time complexity of our proposed algorithm allows the multi-view subspace clustering approach to be applied to large-scale data. Then, we design a four-step alternative optimization algorithm with proven convergence. Compared with state-of-the-art multi-view subspace clustering methods and large-scale oriented methods, the experimental results on several datasets demonstrate that our SMVSC method achieves comparable or better clustering performance much more efficiently. The code of SMVSC is available at https://github.com/Jeaninezpp/SMVSC.

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https://doi.org/10.53469/jrse.2025.07(02).10
Zhu YZhou SJin G(2025)Fast Multi-View Subspace Clustering Based on Flexible Anchor FusionElectronics10.3390/electronics1404073714:4(737)Online publication date: 13-Feb-2025
https://doi.org/10.3390/electronics14040737
Wan XLiu JGan XLiu XWang SWen YWan TZhu E(2025)One-Step Multi-View Clustering With Diverse RepresentationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.337819436:3(5774-5786)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3378194
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Index Terms

Scalable Multi-view Subspace Clustering with Unified Anchors
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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Published In

cover image ACM Conferences

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

Copyright © 2021 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: 17 October 2021

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the Natural Science Foundation of China
National Key R&D Program of China

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MM '21

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SIGMM

MM '21: ACM Multimedia Conference

October 20 - 24, 2021

Virtual Event, China

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

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Cited By

Han QPei STan L(2025)Enhancing Multiview Subspace Clustering with Regularization and Self-Labeling SupervisionJournal of Research in Science and Engineering10.53469/jrse.2025.07(02).107:2(56-61)Online publication date: 27-Feb-2025
https://doi.org/10.53469/jrse.2025.07(02).10
Zhu YZhou SJin G(2025)Fast Multi-View Subspace Clustering Based on Flexible Anchor FusionElectronics10.3390/electronics1404073714:4(737)Online publication date: 13-Feb-2025
https://doi.org/10.3390/electronics14040737
Wan XLiu JGan XLiu XWang SWen YWan TZhu E(2025)One-Step Multi-View Clustering With Diverse RepresentationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.337819436:3(5774-5786)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3378194
Hu SZhang CZou GLou ZYe Y(2025)Deep Multiview Clustering by Pseudo-Label Guided Contrastive Learning and Dual Correlation LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.335473136:2(3646-3658)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2024.3354731
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
Bian JXie XWang CYang LLai JNie F(2025)Angular Reconstructive Discrete Embedding With Fusion Similarity for Multi-View ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348790737:1(45-59)Online publication date: Jan-2025
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Zhang ZCai YGong WLiu XZeng CYu G(2025)MMAGL: Multiobjective Multiview Attributed Graph Learning for Joint Clustering of Hyperspectral and LiDAR DataIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2025.354026963(1-14)Online publication date: 2025
https://doi.org/10.1109/TGRS.2025.3540269
Cao ZLu YXin HWang RNie FSebilo M(2025)Superpixel-Based Bipartite Graph Clustering Enriched With Spatial Information for Hyperspectral and LiDAR DataIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2025.353863263(1-15)Online publication date: 2025
https://doi.org/10.1109/TGRS.2025.3538632
Zhang WHuang XLi AZhang TDing WDeng ZWang S(2025)Dual Anchor Graph Fuzzy Clustering for Multiview DataIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.348902533:2(730-744)Online publication date: Feb-2025
https://doi.org/10.1109/TFUZZ.2024.3489025
Huang XZhang RLi YYang FZhu ZZhou Z(2025)MFC-ACL: Multi-view fusion clustering with attentive contrastive learningNeural Networks10.1016/j.neunet.2024.107055184(107055)Online publication date: Apr-2025
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