[3] [3] XIE X Y, GUO X L, LIU G C, et al.Implicit block diagonal low-rank representation［J］.IEEE Transactions on Image Processing, 2018, 27(1):477-489.

[4] [4] YANG C, REN Z W, SUN Q S, et al.Joint correntropy metric weighting and block diagonal regularizer for robust multiple kernel subspace clustering［J］.Information Sciences, 2019, 500:48-66.

[5] [5] DU L, ZHOU P, SHI L, et al.Robust multiple kernel K-means using L2, 1-norm［C］//Proceedings of the 24th International Joint Conference on Artificial Intelligence, 2015:3476-3482.

[6] [6] WANG S W, LIU X W, ZHU E, et al.Multi-view clustering via late fusion alignment maximization［C］//Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019:3778-3784.

[7] [7] HUANG H C, CHUANG Y Y, CHEN C S.Affinity aggregation for spectral clustering［C］//IEEE Conference on Computer Vision and Pattern Recognition, 2012:773-780.

[8] [8] KANG Z, PENG C, CHENG Q, et al.Unified spectral clustering with optimal graph［C］//The 32nd AAAI Conference on Artificial Intelligence, 2018:1-8.

[9] [9] KANG Z, WEN L, CHEN W, et al.Low-rank kernel learning for graph-based clustering［J］.Knowledge Based Systems, 2019, 163:510-517.

[10] [10] REN Z W, SUN Q S, WU B, et al.Learning latent low-rank and sparse embedding for robust image feature extraction［J］.IEEE Transactions on Image Processing, 2020, 29:2094-2107.

[11] [11] WONG W K, LAI Z H, WEN J J, et al.Low-rank embedding for robust image feature extraction［J］.IEEE Transactions on Image Processing, 2017, 26(6):2905-2917.

[12] [12] BECK A.On the convergence of alternating minimization for convex programming with applications to iteratively reweighted least squares and decomposition schemes［J］.SIAM Journal on Optimization, 2013, 25(1):185-209.

[13] [13] LU C Y, FENG J S, LIN Z C, et al.Subspace clustering by block diagonal representation［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 41(2):487-501.