[1] [1] ZHAO J, XIE X, XU X, et al. Multi-view learning overview[J]. Information Fusion, 2017, 38(C): 43-54

[2] [2] FUTSCHIK M E, Carlisle B. Noise-robust soft clustering of gene expression time-course data[J]. Journal of Bioinformatics and Computational Biology, 2005, 3(04): 965-988

[3] [3] FU L, LIN P, Vasilakos A V, et al. An overview of recent multi-view clustering[J]. Neurocomputing, 2020, 402: 148-161.

[4] [4] SONG J, GUO Y, GAO L, et al. From deterministic to generative: multimodal stochastic RNNs for video captioning[J]. IEEE Trans on Neural Networks and Learning Systems, 2018, 30(10): 3047-3058

[5] [5] CHAO G, SUN S, BI J. A survey on multiview clustering[J]. IEEE transactions on artificial intelligence, 2021, 2(2): 146-168.

[6] [6] CHAO G, SUN S, BI J. et al. Auto-weighted multi-view clustering for large-scale data[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2023, 37(8): 10078-10086.

[8] [8] DU Y, LU G F, JI G. Robust and optimal neighborhood graph learning for multi-view clustering[J]. Information Sciences, 2023, 631: 429-448.

[11] [11] YUN Y, LI J, GAO Q, et al. Low-rank discrete multi-view spectral clustering[J]. Neural Networks, 2023, 166: 137-147.

[12] [12] CHEN Z, WU X J, XU T, et al. Fast self-guided multi-view subspace clustering[J]. IEEE transactions on image processing, 2023.

[14] [14] SU C, YUAN H, LAI L L, et al. Anchor-based multi-view subspace clustering with graph learning[J]. Neurocomputing, 2023, 547: 126320.

[15] [15] LU H, GAO Q, ZHANG X, et al. A multi-view clustering framework via integrating K-means and graph-cut[J]. Neurocomputing, 2022, 501: 609-617.

[17] [17] YU M. Regularized K-means Clustering for Multi-View Data[C]//Journal of Physics: Conference Series. IOP Publishing, 2022, 2381(1): 012036.

[18] [18] ZHENG X, TANG C, LIU X, et al. Multi-view clustering via matrix factorization assisted k-means[J]. Neurocomputing, 2023, 534: 45-54.

[19] [19] BICKEL S, SCHEFFER T. Multi-view clustering[C]//ICDM. 2004, 4(2004): 19-26.

[20] [20] CAI X, NIE F, Huang H. Multi-view k-means clustering on big data[C]//Twenty-Third International Joint Conference on Artificial Intelligence. 2013.

[21] [21] XU J, HAN J, NIE F. Discriminatively embedded k-means for multi-view clustering[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 5356-5364.

[22] [22] XU J, HAN J, NIE F, et al. Re-weighted discriminatively embedded k-means for multi-view clustering[J]. IEEE Trans on Image Processing, 2017, 26(6): 3016-3027

[23] [23] KRIEGEL H P, PEER KRGER, ZIMEK A. Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering[J]. ACM Transactions on Knowledge Discovery from Data, 2009, 3(1): 1-58

[24] [24] KHAN G A, Hu J, Li T, et al. Multi-view low rank sparse representation method for three-way clustering[J]. International Journal of Machine Learning and Cybernetics, 2022, 13: 233-253.

[25] [25] SHEN H T, LIU L, YANG Y, et al. Exploiting subspace relation in semantic labels for cross-modal hashing[J]. IEEE Trans on Knowledge and Data Engineering, 2020, 33(10): 3351-3365

[26] [26] ZHOU Y, TIAN L, ZHU C, et al. Video coding optimization for virtual reality 360-degree source[J]. IEEE Journal of Selected Topics in Signal Processing, 2019, 14(1): 118-129

[27] [27] YANG Y, SHEN H T, Ma Z, et al. 12, 1-norm regularized discriminative feature selection for unsupervised learning[C]//IJCAI International Joint Conference on Artificial Intelligence. 2011.

[28] [28] DING C, HE X, SIMON H D. Nonnegative Lagrangian Relaxation of K-Means and Spectral Clustering[C]//European Conference on Machine Learning. Springer-Verlag, 2005. DOI: 10.1007/11564096_51.

[29] [29] DING C H Q, Li T, Jordan M I. Convex and semi-nonnegative matrix factorizations[J]. IEEE trans on Pattern Analysis and Machine Intelligence, 2008, 32(1): 45-55

[30] [30] JIANG Q, PENG J, WANG H, et al. Tensorial multi-view clustering via low-rank constrained high-order graph learning[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(8): 5307-5318.

[31] [31] ZHU X, ZHANG S, LI Y, et al. Low-Rank Sparse Subspace for Spectral Clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(8): 1532-1543.

[32] [32] DAUBECHIES I, DEVORE R, FORNASIER M, et al. Iteratively re-weighted least squares minimization: proof of faster than linear rate for sparse recovery[C]//2008 42nd Annual Conference on Information Sciences and Systems. IEEE, 2008: 26-29.

[33] [33] CAI D, HE X, HAN J. Document clustering using locality preserving indexing[J]. IEEE Trans on Knowledge and Data Engineering, 2005, 17(12): 1624-1637

[34] [34] V ARSHAVSKY R, LINIAL M, HORN D. Compact: a comparative package for clustering assessment[C]//International Symposium on Parallel and Distributed Processing and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005: 159-167.

[35] [35] CHEN, CHUAN, WANQ YU, HU, WEIBO, et al. Robust multi-view k-means clustering with outlier removal[J]. Knowledge-based systems, 2020, 210(106518): 1-11

[36] [36] HAN J, XU J, NIE F, et al. Multi-view k-means clustering with adaptive sparse memberships and weight allocation[J]. IEEE Trans on Knowledge and Data Engineering, 2020, 34(2): 816-827

[37] [37] WANG H, YANG Y, LIU B. GMC: Graph-based multi-view clustering[J]. IEEE Trans on Knowledge and Data Engineering, 2019, 32(6): 1116-1129

[38] [38] WINN J, JOJIC N. LOCUS: Learning object classes with unsupervised segmentation[C]//Tenth IEEE International Conference on Computer Vision (ICCV' 05) Volume 1. IEEE, 2005, 1: 756-763.

[39] [39] MONADJEMI A, THOMAS B T, MIRMEHDI M. Experiments on high resolution images towards outdoor scene classification[J]. 2002.