research-article

CDD: Multi-view Subspace Clustering via Cross-view Diversity Detection

Authors:

Quanhui LiuAuthors Info & Claims

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

Pages 2308 - 2316

https://doi.org/10.1145/3474085.3475393

Published: 17 October 2021 Publication History

Abstract

The goal of multi-view subspace clustering is to explore a common latent space where the multi-view data points lying on. Myriads of subspace learning algorithms have been investigated to boost the performance of multi-view clustering, but seldom exploiting both the multi-view consistency and multi-view diversity, let alone taking them into consideration simultaneously. To do so, we lodge a novel multi-view subspace clustering via cross-view diversity detection (CDD). CDD is able to exploit these two complementary criteria seamlessly into a holistic design of clustering algorithms. With the consistent part and diverse part being detected, a pure graph can be derived for each view. The consistent pure parts of different views are further fused to a consensus structured graph with exactly k connected components where k is the number of clusters. Thus we can directly obtain the final clustering result without any postprocessing as each connected component precisely corresponds to an individual cluster. We model the above concerns into a unified optimization framework. Our empirical studies validate that the proposed model outperforms several other state-of-the-art methods.

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Yu SLiu SWang STang CLuo ZLiu XZhu E(2025)Sparse Low-Rank Multi-View Subspace Clustering With Consensus Anchors and Unified Bipartite GraphIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.333233536:1(1438-1452)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2023.3332335
You JRen ZYu FYou X(2024)One-Stage Shifted Laplacian Refining for Multiple Kernel ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.326259035:8(11501-11513)Online publication date: Aug-2024
https://doi.org/10.1109/TNNLS.2023.3262590
Wu SWu AZheng W(2024)Online Multi-View Learning With Knowledge Registration UnitsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.325639035:9(12301-12315)Online publication date: Sep-2024
https://doi.org/10.1109/TNNLS.2023.3256390
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Index Terms

CDD: Multi-view Subspace Clustering via Cross-view Diversity Detection

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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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Published: 17 October 2021

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MM '21: ACM Multimedia Conference

October 20 - 24, 2021

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Cited By

Yu SLiu SWang STang CLuo ZLiu XZhu E(2025)Sparse Low-Rank Multi-View Subspace Clustering With Consensus Anchors and Unified Bipartite GraphIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.333233536:1(1438-1452)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2023.3332335
You JRen ZYu FYou X(2024)One-Stage Shifted Laplacian Refining for Multiple Kernel ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.326259035:8(11501-11513)Online publication date: Aug-2024
https://doi.org/10.1109/TNNLS.2023.3262590
Wu SWu AZheng W(2024)Online Multi-View Learning With Knowledge Registration UnitsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.325639035:9(12301-12315)Online publication date: Sep-2024
https://doi.org/10.1109/TNNLS.2023.3256390
Huang STsang IXu ZLv J(2024)CGDD: Multiview Graph Clustering via Cross-Graph Diversity DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.320196435:3(4206-4219)Online publication date: Mar-2024
https://doi.org/10.1109/TNNLS.2022.3201964
Tan YLiu YWu HHuang SXu ZTsang ILv J(2024)Euclidean Distance is Not Your Swiss Army KnifeIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342451136:12(8179-8191)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3424511
Lin JChen MWang CZhang H(2024)A Tensor Approach for Uncoupled Multiview ClusteringIEEE Transactions on Cybernetics10.1109/TCYB.2022.321248054:2(1236-1249)Online publication date: Feb-2024
https://doi.org/10.1109/TCYB.2022.3212480
Zhao LXie Q(2024)Distribution-Level Multi-View Clustering for Unaligned DataIEEE Signal Processing Letters10.1109/LSP.2024.344094831(2330-2334)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3440948
Zhang CChen HLi HChen C(2024)Learning latent disentangled embeddings and graphs for multi-view clusteringPattern Recognition10.1016/j.patcog.2024.110839156(110839)Online publication date: Dec-2024
https://doi.org/10.1016/j.patcog.2024.110839
Huang SLiu YTsang IXu ZLv J(2023)Multi-View Subspace Clustering by Joint Measuring of Consistency and DiversityIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.319958735:8(8270-8281)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3199587
Yang WWang YTang CTong HWei AWu X(2023)One step multi-view spectral clustering via joint adaptive graph learning and matrix factorizationNeurocomputing10.1016/j.neucom.2022.12.023524:C(95-105)Online publication date: 1-Mar-2023
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