BLUM A, MITCHELL T M. Combining labeled and unla-beled data with co-training[C]//Proceedings of the 11th Annual Conference on Computational Learning Theory, Madison, Jul 24-26, 1998. New York: ACM, 1998: 92-100.
 SCHECHTER I, WAKELING T, WOLLRATH A M. Proce-ssing data from multiple sources: U.S. Patent 9607073[P]. 2017-03-28.
 HU M, CHEN S. Doubly aligned incomplete multi-view clus-tering[C]//Proceedings of the 27th International Joint Con-ference on Artificial Intelligence, Stockholm, Jul 13-19, 2018: 2262-2268.
 XU C, TAO D, XU C. A survey on multi-view learning[J]. arXiv:1304.5634, 2013.
 XU C, TAO D C, XU C. Multi-view learning with incomp-lete views[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5812-5825.
 BREFELD U, G?RTNER T, SCHEFFER T, et al. Efficient co-regularised least squares regression[C]//Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, Jun 25-29, 2006. New York: ACM, 2006: 137-144.
 SINDHWANI V, NIYOGI P, BELKIN M. A co-regulari-zation approach to semi-supervised learning with multiple views[C]//Proceedings of the 2005 Workshop on Learning with Multiple Views, Bonn, 2005: 74-79.
 NIGAM K, GHANI R. Analyzing the effectiveness and app-licability of co-training[C]//Proceedings of the 2000 ACM CIKM International Conference on Information and Know-ledge Management, McLean, Nov 6-11, 2000. New York: ACM, 2000: 86-93.
 WANG W, ZHOU Z H. A new analysis of co-training[C]//Proceedings of the 27th International Conference on Machine Learning, Haifa, Jun 21-24, 2010. Madison: Omnipress, 2010: 1135-1142.
 LANCKRIET G R G, CRISTIANINI N, BARTLETT P, et al. Learning the kernel matrix with semidefinite programming[J]. Journal of Machine Learning Research, 2004, 5: 27-72.
 BACH F R, LANCKRIET G R G, JORDAN M I. Multiple kernel learning, conic duality, and the SMO algorithm[C]//Proceedings of the 21st International Conference on Mach-ine Learning, Banff, Jul 4-8, 2004. New York: ACM, 2004: 6.
 SONNENBURG S, R?TSCH G, SCH?FER C, et al. Large scale multiple kernel learning[J]. Journal of Machine Lear-ning Research, 2006, 7(7): 1531-1565.
 RAKOTOMAMONJY A, BACH F R, CANU S, et al. Simple-MKL[J]. Journal of Machine Learning Research, 2008, 9(11): 2491-2521.
 CHAUDHURI K, KAKADE S M, LIVESCU K, et al. Multi-view clustering via canonical correlation analysis[C]//Proc-eedings of the 26th Annual International Conference on Mac-hine Learning, Montreal, Jun 14-18, 2009. New York: ACM, 2009: 129-136.
 SIGAL L, MEMISEVIC R, FLEET D J. Shared kernel infor-mation embedding for discriminative inference[C]//Procee-dings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, Jun 20-25, 2009. Washington: IEEE Computer Society, 2009: 2852-2859.
 ZHU X Z, LIU X W, LI M M, et al. Localized incomplete multiple kernel k-means[C]//Proceedings of the 27th Inter-national Joint Conference on Artificial Intelligence, Stockholm, Jul 13-19, 2018: 3271-3277.
 YANG W Q, SHI Y H, GAO Y, et al. Incomplete-data oriented multiview dimension reduction via sparse low-rank represen-tation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(12): 6276-6291.
 ZHAO H D, LIU H F, FU Y. Incomplete multi-modal visual data grouping[C]//Proceedings of the 25th International Joint Conference on Artificial Intelligence, New York, Jul 9-15, 2016. Menlo Park: AAAI, 2016: 2392-2398.
 YIN Q Y, WU S, WANG L. Unified subspace learning for incomplete and unlabeled multi-view data[J]. Pattern Recogni-tion, 2017, 67: 313-327.
 THUNG K H, ADELI E, YAP P T, et al. Stability-weighted matrix completion of incomplete multi-modal data for disease diagnosis[C]//LNCS 9901: Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Oct 17-21, 2016. Berlin, Heidelberg: Springer, 2016: 88-96.
 ZANTEDESCHI V, EMONET R, SEBBAN M. Fast and provably effective multi-view classification with landmark-based SVM[C]//LNCS 11052: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, Dublin, Sep 10-14, 2018. Cham: Springer, 2018: 193-208.
 ZHAO J, XIE X, XU X, et al. Multi-view learning overview: recent progress and new challenges[J]. Information Fusion, 2017, 38: 43-54.
 YU S P, KRISHNAPURAM B, ROSALES R, et al. Bayesian co-training[C]//Proceedings of the 21st Annual Conference on Neural Information Processing Systems, Vancouver, Dec 3-6, 2007. Red Hook: Curran Associates, 2007: 1665-1672.
 ZHAO X R, EVANS N W D, DUGELAY J L. A subspace co-training framework for multi-view clustering[J]. Pattern Reco-gnition Letters, 2014, 41: 73-82.
 DING Z M, FU Y. Low-rank common subspace for multi-view learning[C]//Proceedings of the 2014 IEEE International Conference on Data Mining, Shenzhen, Dec 14-17, 2014: 110-119.
 GUO Y H. Convex subspace representation learning from multi-view data[C]//Proceedings of the 27th AAAI Conference on Artificial Intelligence, Bellevue, Jul 14-18, 2013. Menlo Park: AAAI, 2013: 387-393.
 YIN Q Y, WU S, WANG L. Incomplete multi-view cluster-ing via subspace learning[C]//Proceedings of the 24th ACM International Conference on Information and Knowledge Mana-gement, Melbourne, Oct 19-23, 2015. New York: ACM, 2015: 383-392.
 SHAO W X, HE L F, PHILIP S Y. Multiple incomplete views clustering via weighted nonnegative matrix factorization with L2,1 regularization[C]//LNCS 9284: Proceedings of the Euro-pean Conference on Machine Learning and Knowledge Dis-covery in Databases, Porto, Sep 7-11, 2015. Cham: Springer, 2015: 318-334.
 LI S Y, JIANG Y, ZHOU Z H. Partial multi-view clustering[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence, Québec City, Jul 27-31, 2014. Menlo Park: AAAI, 2014: 1968-1974.
 YANG Y, ZHAN D C, SHENG X R, et al. Semi-supervised multi-modal learning with incomplete modalities[C]//Procee-dings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Jul 13-19, 2018: 2998-3004.
 YANG Y, ZHAN D C, WU Y F, et al. Semi-supervised multi-modal clustering and classification with incomplete modalities[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(2): 682-695.
 BECK A, TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(1): 183-202.
 SUN Q S, ZENG S G, LIU Y, et al. A new method of feature fusion and its application in image recognition[J]. Pattern Recognition, 2005, 38(12): 2437-2448.
 SHEKHAR S, PATEL V M, NASRABADI N M, et al. Joint sparse representation for robust multimodal biometrics recog-nition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 36(1): 113-126.