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Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views.
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2006
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2007
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2010
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2013
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W. Yang, Y. Gao, Y. Shi, and L. Cao, “Mrm-lasso: A sparse multiview feature selection method via low-rank analysis,” IEEE Transactions on Neural Networks and Learning Systems , vol. 26, no. 11, pp. 2801–2815, 2015
2015
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H. Gao, F. Nie, X. Li, and H. Huang, “Multi-view subspace clustering,” in 2015 IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 4238–4246
2015
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H. Wang, Y. Yang, and T. Li, “Multi-view clustering via concept factorization with local manifold regularization,” in 2016 IEEE 16th International Conference on Data Mining (ICDM) , 2016, pp. 1245–1250
2016
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Y. Wang, W. Zhang, L. Wu, X. Lin, and X. Zhao, “Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion,” IEEE Transactions on Neural Networks and Learning Systems , vol. 28, no. 1, pp. 57–70, 2017
2017
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C. Zhang, Q. Hu, H. Fu, P. Zhu, and X. Cao, “Latent multi-view subspace clustering,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 4333–4341
2017
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2017
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Z. Ding and Y. Fu, “Robust multiview data analysis through collective low-rank subspace,” IEEE Transactions on Neural Networks and Learning Systems , vol. 29, no. 5, pp. 1986–1997, 2018
2018
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Y. Wang, L. Wu, X. Lin, and J. Gao, “Multiview spectral clustering via structured low-rank matrix factorization,” IEEE Transactions on Neural Networks and Learning Systems , vol. 29, no. 10, pp. 4833–4843, 2018
2018
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P. Luo, J. Peng, Z. Guan, and J. Fan, “Dual regularized multi-view non-negative matrix factorization for clustering,” Neurocomputing , vol. 294, pp. 1–11, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231217316740
2018
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J. Wang, F. Tian, H. Yu, C. H. Liu, K. Zhan, and X. Wang, “Diverse non-negative matrix factorization for multiview data representation5,” IEEE Transactions on Cybernetics , vol. 48, no. 9, pp. 2620–2632, 2018
2018
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Q. Wang, Z. Qin, F. Nie, and X. Li, “Spectral embedded adaptive neighbors clustering,” IEEE Transactions on Neural Networks and Learning Systems , vol. 30, no. 4, pp. 1265–1271, 2019
2019
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M. Yin, J. Gao, S. Xie, and Y. Guo, “Multiview subspace clustering via tensorial t-product representation,” IEEE Transactions on Neural Networks and Learning Systems , vol. 30, no. 3, pp. 851–864, 2019
2019
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G. A. Khan, J. Hu, T. Li, B. Diallo, and Q. Huang, “Weighted multi-view data clustering via joint non-negative matrix factorization,” in 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) , 2019, pp. 1159–1165
2019
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S. Gao, Z. Yu, T. Jin, and M. Yin, “Multi-view low-rank matrix factorization using multiple manifold regularization,” Neurocomputing , vol. 335, pp. 143–152, 2019
2019
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N. Liang, Z. Yang, Z. Li, S. Xie, and C.-Y. Su, “Semi-supervised multi-view clustering with graph-regularized partially shared non-negative matrix factorization,” Know.-Based Syst. , vol. 190, no. C, feb 2020. [Online]. Available: https://doi.org/10.1016/j.knosys.2019.105185
2019
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Z. Zhang, L. Liu, F. Shen, H. T. Shen, and L. Shao, “Binary multi-view clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 41, no. 7, pp. 1774–1782, 2019
2019
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S. Wang, X. Liu, E. Zhu, C. Tang, J. Liu, J. Hu, J. Xia, and J. Yin, “Multi-view clustering via late fusion alignment maximization,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 . International Joint Conferences on Artificial Intelligence Organization, 7 2019, pp. 3778–3784. [Online]. Available: https://doi.org/10.24963/ijcai.2019/524
2019
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R. Li, C. Zhang, Q. Hu, P. Zhu, and Z. Wang, “Flexible multi-view representation learning for subspace clustering,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 . International Joint Conferences on Artificial Intelligence Organization, 7 2019, pp. 2916–2922. [Online]. Available: https://doi.org/10.24963/ijcai.2019/404
2019
Cited alongside, same era.
Z. Tao, H. Liu, S. Li, Z. Ding, and Y. Fu, “Marginalized multiview ensemble clustering,” IEEE Transactions on Neural Networks and Learning Systems , vol. 31, no. 2, pp. 600–611, 2020
2020
Cited alongside, same era.
X. Peng, H. Zhu, J. Feng, C. Shen, H. Zhang, and J. T. Zhou, “Deep clustering with sample-assignment invariance prior,” IEEE Transactions on Neural Networks and Learning Systems , vol. 31, no. 11, pp. 4857–4868, 2020
2020
Cited alongside, same era.
L. Huang, C.-D. Wang, H.-Y. Chao, and P. S. Yu, “Mvstream: Multiview data stream clustering,” IEEE Transactions on Neural Networks and Learning Systems , vol. 31, no. 9, pp. 3482–3496, 2020
J. Han, J. Xu, F. Nie, and X. Li, “Multi-view k-means clustering with adaptive sparse memberships and weight allocation,” IEEE Transactions on Knowledge and Data Engineering , vol. 34, no. 2, pp. 816–827, 2022
2022
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2022
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S. Huang, Y. Liu, I. W. Tsang, Z. Xu, and J. Lv, “Multi-view subspace clustering by joint measuring of consistency and diversity,” IEEE Transactions on Knowledge and Data Engineering , pp. 1–12, 2022
2022
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C. Zhang, Y. Cui, Z. Han, J. T. Zhou, H. Fu, and Q. Hu, “Deep partial multi-view learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 5, pp. 2402–2415, 2022
2022
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2020
Cited alongside, same era.
Z. Yue, H. Yong, D. Meng, Q. Zhao, Y. Leung, and L. Zhang, “Robust multiview subspace learning with nonindependently and nonidentically distributed complex noise,” IEEE Transactions on Neural Networks and Learning Systems , vol. 31, no. 4, pp. 1070–1083, 2020
2020
Cited alongside, same era.
W. Liang, S. Zhou, J. Xiong, X. Liu, S. Wang, E. Zhu, Z. Cai, and X. Xu, “Multi-view spectral clustering with high-order optimal neighborhood laplacian matrix,” in IEEE Transactions on Knowledge and Data Engineering (TKDE) , 2020
2020
Cited alongside, same era.
C. Tang, X. Liu, X. Zhu, E. Zhu, Z. Luo, L. Wang, and W. Gao, “Cgd: Multi-view clustering via cross-view graph diffusion,” in AAAI Conference on Artificial Intelligence (AAAI) , 2020, pp. 5924–5931
2020
Cited alongside, same era.
N. Liang, Z. Yang, Z. Li, W. Sun, and S. Xie, “Multi-view clustering by non-negative matrix factorization with co-orthogonal constraints,” Knowledge-Based Systems , vol. 194, p. 105582, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0950705120300642
2020
Cited alongside, same era.
J.-W. Liu, Y.-F. Wang, R.-K. Lu, and X.-L. Luo, “Multi-view non-negative matrix factorization discriminant learning via cross entropy loss,” in 2020 Chinese Control And Decision Conference (CCDC) , 2020, pp. 3964–3971
2020
Cited alongside, same era.
Q. Ou, S. Wang, S. Zhou, M. Li, X. Guo, and E. Zhu, “Anchor-based multiview subspace clustering with diversity regularization,” IEEE MultiMedia , vol. 27, no. 4, pp. 91–101, 2020
2020
Cited alongside, same era.
Z. Kang, W. Zhou, Z. Zhao, J. Shao, M. Han, and Z. Xu, “Large-scale multi-view subspace clustering in linear time,” in AAAI , 2020
2020
Cited alongside, same era.
S. Wang, X. Liu, L. Liu, S. Zhou, and E. Zhu, “Late fusion multiple kernel clustering with proxy graph refinement,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–12, 2021
2021
Cited alongside, same era.
S. Liu, S. Wang, P. Zhang, K. Xu, X. Liu, C. Zhang, and F. Gao, “Efficient one-pass multi-view subspace clustering with consensus anchors,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 7, 2022, pp. 7576–7584
2022
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L. Li, S. Wang, X. Liu, E. Zhu, L. Shen, K. Li, and K. Li, “Local sample-weighted multiple kernel clustering with consensus discriminative graph,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–14, 2022
2022
Later among the works it cites.
J. Zhang, L. Li, S. Wang, J. Liu, Y. Liu, X. Liu, and E. Zhu, “Multiple kernel clustering with dual noise minimization,” in Proceedings of the 30th ACM International Conference on Multimedia , ser. MM ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 3440–3450
2022
Later among the works it cites.
X. Wan, J. Liu, W. Liang, X. Liu, Y. Wen, and E. Zhu, “Continual multi-view clustering,” in Proceedings of the 30th ACM International Conference on Multimedia , ser. MM ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 3676–3684. [Online]. Available: https://doi.org/10.1145/3503161.3547864
2022
Later among the works it cites.
Y. Liu, W. Tu, S. Zhou, X. Liu, L. Song, X. Yang, and E. Zhu, “Deep graph clustering via dual correlation reduction,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 7, 2022, pp. 7603–7611
2022
Later among the works it cites.
2022
Later among the works it cites.
W. Xia, Q. Gao, Q. Wang, X. Gao, C. Ding, and D. Tao, “Tensorized bipartite graph learning for multi-view clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Later among the works it cites.
X. Li, H. Zhang, R. Wang, and F. Nie, “Multiview clustering: A scalable and parameter-free bipartite graph fusion method,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 1, pp. 330–344, 2022
2022
Later among the works it cites.
C. Tang, Z. Li, J. Wang, X. Liu, W. Zhang, and E. Zhu, “Unified one-step multi-view spectral clustering,” IEEE Transactions on Knowledge and Data Engineering , pp. 1–1, 2022
2022
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P. Zhang, X. Liu, J. Xiong, S. Zhou, W. Zhao, E. Zhu, and Z. Cai, “Consensus one-step multi-view subspace clustering,” IEEE Transactions on Knowledge and Data Engineering , vol. 34, no. 10, pp. 4676–4689, 2022
2022
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S. Pei, H. Chen, F. Nie, R. Wang, and X. Li, “Centerless clustering: An efficient variant of k-means based on k-nn graph,” IEEE transactions on pattern analysis and machine intelligence , vol. PP, February 2022. [Online]. Available: https://doi.org/10.1109/TPAMI.2022.3150981
2022
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H. Xu, L. Gong, H. Xuan, X. Zheng, Z. Gao, and X. Wen, “Multiview clustering via consistent and specific nonnegative matrix factorization with graph regularization,” Multimedia Syst. , vol. 28, no. 5, p. 1559–1572, oct 2022. [Online]. Available: https://doi-org-s.libyc.nudt.edu.cn:443/10.1007/s00530-022-00905-x
2022
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S. Wang, X. Liu, X. Zhu, P. Zhang, Y. Zhang, F. Gao, and E. Zhu, “Fast parameter-free multi-view subspace clustering with consensus anchor guidance,” IEEE Transactions on Image Processing , vol. 31, pp. 556–568, 2022
2022
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U. Fang, M. Li, J. Li, L. Gao, T. Jia, and Y. Zhang, “A comprehensive survey on multi-view clustering,” IEEE Transactions on Knowledge and Data Engineering , pp. 1–20, 2023
2023
Closest in time.
J. Xu, Y. Ren, H. Tang, Z. Yang, L. Pan, Y. Yang, X. Pu, P. S. Yu, and L. He, “Self-supervised discriminative feature learning for deep multi-view clustering,” IEEE Transactions on Knowledge and Data Engineering , vol. 35, no. 7, pp. 7470–7482, 2023
2023
Closest in time.
X. Wan, X. Liu, J. Liu, S. Wang, Y. Wen, W. Liang, E. Zhu, Z. Liu, and L. Zhou, “Auto-weighted multi-view clustering for large-scale data,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2023
2023
Closest in time.