A journal of IEEE and CAA , publishes
high-quality papers in English on original
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and development in all areas of automation
Volume 9
Issue 11
IEEE/CAA Journal of Automatica Sinica
| Citation: | S. P. Wang, X. C. Lin, Z. H. Fang, S. D. Du, and G. B. Xiao, “Contrastive consensus graph learning for multi-view clustering,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 2027–2030, Nov. 2022. doi: 10.1109/JAS.2022.105959
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T. Zhou, M. Chen, and J. Zou, “Reinforcement learning based data fusion method for multi-sensors,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1489–1497, 2020. doi: 10.1109/JAS.2020.1003180
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Y. Wang, Z. Zhang, and Y. Lin, “Multi-cluster feature selection based on isometric mapping,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 570–572, 2021.
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| [3] |
F. Nie, J. Li, and X. Li, “Self-weighted multiview clustering with multiple graphs,” in Proc. Int. Joint Conf. Artificial Intelligence, 2017, pp. 2564–2570.
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| [4] |
S. Wang, Z. Chen, S. Du, and Z. Lin, “Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, pp. 1–14, 2021, DOI: 10.1109/TPAMI.2021.3082632.
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S. Du, Z. Liu, Z. Chen, W. Yang, and S. Wang, “Differentiable bi-sparse multi-view co-clustering,” IEEE Trans. Signal Processing, vol. 69, pp. 4623–4636, 2021. doi: 10.1109/TSP.2021.3101979
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Z. Li, C. Tang, X. Liu, X. Zheng, G. Yue, W. Zhang, and E. Zhu, “Consensus graph learning for multi-view clustering,” IEEE Trans. Multimedia, pp. 1–12, 2021, DOI: 10.1109/TMM.2021.3081930.
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H. Wang, Y. Yang, and B. Liu, “GMC: Graph-based multi-view clustering,” IEEE Trans. Knowledge and Data Engineering, vol. 32, no. 6, pp. 1116–1129, 2019.
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C. Tang, X. Zhu, X. Liu, M. Li, Wa ng, C. Zhang, and L. Wang, “Learning a joint affinity graph for multiview subspace clustering,” IEEE Trans. Multimedia, vol. 21, no. 7, pp. 1724–1736, 2018.
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K. Zhan, F. Nie, J. Wang, and Y. Yang, “Multiview consensus graph clustering,” IEEE Trans. Image Processing, vol. 28, no. 3, pp. 1261–1270, 2019. doi: 10.1109/TIP.2018.2877335
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A. Benton, H. Khayrallah, B. Gujral, D. A. Reisinger, S. Zhang, and R. Arora, “Deep generalized canonical correlation analysis,” in Proc. Workshop Representation Learning for NLP, 2019, pp. 1–6.
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H. Zhao, Z. Ding, and Y. Fu, “Multi-view clustering via deep matrix factorization,” in Proc. AAAI Conf. Artificial Intelligence, 2017, pp. 2921–2927.
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Z. Huang, J. T. Zhou, X. Peng, C. Zhang, H. Zhu, and J. Lv, “Multi-view spectral clustering network,” in Proc. Int. Joint Conf. Artificial Intelligence, 2019, pp. 2563–2569.
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Y. Li, P. Hu, Z. Liu, D. Peng, J. T. Zhou, and X. Peng, “Contrastive clustering,” in Proc. AAAI Conf. Artificial Intelligence, 2021, pp. 8547–8555.
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H. Zhong, J. Wu, C. Chen, J. Huang, M. Deng, L. Nie, Z. Lin, and X.-S. Hua, “Graph contrastive clustering,” in Proc. IEEE/CVF Int. Conf. Computer Vision, 2021, pp. 9224–9233.
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Y. Lin, Y. Gou, Z. Liu, B. Li, J. Lv, and X. Peng, “COMPLETER: Incomplete multi-view clustering via contrastive prediction,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2021, pp. 11174–11183.
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C. Zhang, H. Fu, J. Wang, W. Li, X. Cao, and Q. Hu, “Tensorized multi-view subspace representation learning,” Int. J. Computer Vision, vol. 128, no. 8, pp. 2344–2361, 2020.
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Z. Zhang, L. Liu, F. Shen, H. T. Shen, and L. Shao, “Binary multi-view clustering,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 41, no. 7, pp. 1774–1782, 2019. doi: 10.1109/TPAMI.2018.2847335
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