research-article

Self-Representation Subspace Clustering for Incomplete Multi-view Data

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Yuexiang YangAuthors Info & Claims

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

Pages 2726 - 2734

https://doi.org/10.1145/3474085.3475379

Published: 17 October 2021 Publication History

Abstract

Incomplete multi-view clustering is an important research topic in multimedia where partial data entries of one or more views are missing. Current subspace clustering approaches mostly employ matrix factorization on the observed feature matrices to address this issue. Meanwhile, self-representation technique is left unexplored, since it explicitly relies on full data entries to construct the coefficient matrix, which is contradictory to the incomplete data setting. However, it is widely observed that self-representation subspace method enjoys a better clustering performance over the factorization based one. Therefore, we adapt it to incomplete data by jointly performing data imputation and self-representation learning. To the best of our knowledge, this is the first attempt in incomplete multi-view clustering literature. Besides, the proposed method is carefully compared with current advances in experiment with respect to different missing ratios, verifying its effectiveness.

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Self-Representation Subspace Clustering for Incomplete Multi-view Data

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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

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Published: 17 October 2021

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MM '21

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MM '21: ACM Multimedia Conference

October 20 - 24, 2021

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Cited By

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https://doi.org/10.1109/TNNLS.2024.3350671
Fu YLi YHuang QCui JWen J(2025)Anchor Graph Network for Incomplete Multiview ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.334940536:2(3708-3719)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2024.3349405
Zhang XPu XChe HLiu CQin J(2025)Two-step graph propagation for incomplete multi-view clusteringNeural Networks10.1016/j.neunet.2024.106944183(106944)Online publication date: Mar-2025
https://doi.org/10.1016/j.neunet.2024.106944
Wang ZDu YWang YNing RLi L(2025)Deep Incomplete Multi-view Clustering via Multi-level Imputation and Contrastive AlignmentNeural Networks10.1016/j.neunet.2024.106851181(106851)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106851
Ou ZDeng XChen LDeng J(2025)Adaptive multi-view subspace clustering algorithm based on representative features and redundant instancesNeurocomputing10.1016/j.neucom.2024.128839620(128839)Online publication date: Mar-2025
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Yang BSong PCheng YLiu ZYu Y(2025)Label completion based concept factorization for incomplete multi-view clusteringKnowledge-Based Systems10.1016/j.knosys.2025.112953310(112953)Online publication date: Feb-2025
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Liu WJing XZeng DZhang T(2025)Graph refinement and consistency self-supervision for tensorized incomplete multi-view clusteringInformation Fusion10.1016/j.inffus.2024.102709114(102709)Online publication date: Feb-2025
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Zhan JZhu YLuo G(2025)BACVC: Bi-adaptive and cross-view consistency for incomplete multi-view subspace clusteringAlexandria Engineering Journal10.1016/j.aej.2025.01.089119(623-633)Online publication date: Apr-2025
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Gu ZLi ZFeng SSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)EDISONProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692728(16548-16567)Online publication date: 21-Jul-2024
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