Article

Robust multi-view spectral clustering via low-rank and sparse decomposition

Authors:

Jian YinAuthors Info & Claims

AAAI'14: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence

Pages 2149 - 2155

Published: 27 July 2014 Publication History

Abstract

Multi-view clustering, which seeks a partition of the data in multiple views that often provide complementary information to each other, has received considerable attention in recent years. In real life clustering problems, the data in each view may have considerable noise. However, existing clustering methods blindly combine the information from multi-view data with possibly considerable noise, which often degrades their performance. In this paper, we propose a novel Markov chain method for Robust Multi-view Spectral Clustering (RMSC). Our method has a flavor of lowrank and sparse decomposition, where we firstly construct a transition probability matrix from each single view, and then use these matrices to recover a shared low-rank transition probability matrix as a crucial input to the standard Markov chain method for clustering. The optimization problem of RMSC has a low-rank constraint on the transition probability matrix, and simultaneously a probabilistic simplex constraint on each of its rows. To solve this challenging optimization problem, we propose an optimization procedure based on the Augmented Lagrangian Multiplier scheme. Experimental results on various real world datasets show that the proposed method has superior performance over several state-of-the-art methods for multi-view clustering.

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Cited By

Li XPan YSun YSun QTsang IRen ZLarson K(2024)Fast unpaired multi-view clusteringProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/496(4488-4496)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/496
Feng WSheng GWang QGao QTao ZDong BWooldridge MDy JNatarajan S(2024)Partial multi-view clustering via self-supervised networkProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i11.29086(11988-11995)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i11.29086
Zhou PDu LEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Learnable Graph Filter for Multi-view ClusteringProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611912(3089-3098)Online publication date: 26-Oct-2023
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Robust multi-view spectral clustering via low-rank and sparse decomposition
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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AAAI'14: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence

July 2014

3155 pages

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AAAI Press

Publication History

Published: 27 July 2014

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Cited By

Li XPan YSun YSun QTsang IRen ZLarson K(2024)Fast unpaired multi-view clusteringProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/496(4488-4496)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/496
Feng WSheng GWang QGao QTao ZDong BWooldridge MDy JNatarajan S(2024)Partial multi-view clustering via self-supervised networkProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i11.29086(11988-11995)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i11.29086
Zhou PDu LEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Learnable Graph Filter for Multi-view ClusteringProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611912(3089-3098)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611912
Chen YWang SPeng CLu GZhou YShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)Partial Tubal Nuclear Norm Regularized Multi-view LearningProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475699(1341-1349)Online publication date: 17-Oct-2021
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Wang QXia WTao ZGao QCao XShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)Deep Self-Supervised t-SNE for Multi-modal Subspace ClusteringProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475319(1748-1755)Online publication date: 17-Oct-2021
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Abhadiomhen SWang ZShen XFan J(2021)Multiview Common Subspace Clustering via Coupled Low Rank RepresentationACM Transactions on Intelligent Systems and Technology10.1145/346505612:4(1-25)Online publication date: 1-Aug-2021
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You JHu CKamigaito HFunakoshi KOkumura MDemartini GZuccon GCulpepper JHuang ZTong H(2021)Robust Dynamic Clustering for Temporal NetworksProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482473(2424-2433)Online publication date: 26-Oct-2021
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Fanseu Kamhoua BZhang LMa KCheng JLi BHan BDemartini GZuccon GCulpepper JHuang ZTong H(2021)HyperGraph Convolution Based Attributed HyperGraph ClusteringProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482437(453-463)Online publication date: 26-Oct-2021
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Shen XZhou JMa ZBao BZha Z(2021)Cross-Domain Object Representation via Robust Low-Rank Correlation AnalysisACM Transactions on Multimedia Computing, Communications, and Applications10.1145/345882517:4(1-20)Online publication date: 12-Nov-2021
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Wang Y(2021)Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry, and FusionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/340831717:1s(1-25)Online publication date: 31-Mar-2021
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