[22] Li, Y., Nie, F., Huang, H., and Huang, J. Large-scale multi-view spectral clustering via bipartite graph. In Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
[23] Liu, J., Wang, C., Gao, J., and Han, J. Multi-view clustering via joint nonnegative matrix factorization. In Proceedings of the 2013 SIAM international conference on data mining, pp. 252-260. SIAM, 2013.
[24] Luo, S., Zhang, C., Zhang, W., and Cao, X. Consistent and specific multi-view subspace clustering. In Thirty-second AAAI conference on artificial intelligence, 2018.
[25] Nie, F., Li, J., Li, X., et al. Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In IJCAI, pp. 1881-1887, 2016.
[26] Oyelade, O. J., Oladipupo, O. O., and Obagbuwa, I. C. Application of k means clustering algorithm for prediction of students academic performance. arXiv preprint arXiv:1002.2425, 2010.
[27] Pauca, V. P., Piper, J., and Plemmons, R. J. Nonnegative matrix factorization for spectral data analysis. Linear algebra and its applications, 416(1):29-47, 2006.
[28] Wang, H., Nie, F., Huang, H., and Makedon, F. Fast nonnegative matrix tri-factorization for large-scale data co-clustering. In Twenty-Second International Joint Conference on Artificial Intelligence, 2011.
[29] Wang, H., Yang, Y., and Li, T. Multi-view clustering via concept factorization with local manifold regularization. In 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1245-1250. IEEE, 2016.
[30] Wang, H., Yang, Y., and Liu, B. Gmc: Graph-based multi-view clustering. IEEE Transactions on Knowledge and Data Engineering, 32(6):1116-1129, 2019.
[31] Wang, S., Liu, X., Zhu, X., Zhang, P., Zhang, Y., Gao, F., and Zhu, E. Fast parameter-free multi-view subspace clustering with consensus anchor guidance. IEEE Transactions on Image Processing, 31:556-568, 2021.
[32] Wang, W., Arora, R., Livescu, K., and Bilmes, J. A. On deep multi-view representation learning. In ICML, volume 37, pp. 1083-1092, 2015.
[33] Wang, Z., Kong, X., Fu, H., Li, M., and Zhang, Y. Feature extraction via multi-view non-negative matrix factorization with local graph regularization. In 2015 IEEE International conference on image processing (ICIP), pp. 3500-3504. IEEE, 2015.
[34] Winn, J. M. and Joic, N. LOCUS: learning object classes with unsupervised segmentation. pp. 756-763, 2005.
[35] Wu, J., Lin, Z., and Zha, H. Essential tensor learning for multi-view spectral clustering. 28(12):5910-5922, 2019.
[36] Xie, Y., Gu, S., Liu, Y., Zuo, W., Zhang, W., and Zhang, L. Weighted schatten  $p$ -norm minimization for image denoising and background subtraction. IEEE Trans. Image Process., 25(10):4842-4857, 2016.
[37] Yang, B., Zhang, X., Nie, F., Wang, F., Yu, W., and Wang, R. Fast multi-view clustering via nonnegative and orthogonal factorization. IEEE transactions on image processing, 30:2575-2586, 2021.
[38] Yang, H., Gao, Q., Xia, W., Yang, M., and Gao, X. Multiview spectral clustering with bipartite graph. IEEE Transactions on Image Processing, 31:3591-3605, 2022.
[39] Zha, Z., Yuan, X., Wen, B., Zhou, J., Zhang, J., and Zhu, C. A benchmark for sparse coding: When group sparsity meets rank minimization. IEEE Transactions on Image Processing, 29:5094-5109, 2020.
[40] Zhan, K., Zhang, C., Guan, J., and Wang, J. Graph learning for multiview clustering. IEEE transactions on cybernetics, 48(10):2887-2895, 2017.