Abstract：In most of the existing multi-view subspace clustering methods, the consistent shared information of the multi-view data is learned, and the contribution of each view is regarded as equally important to integrate the difference information of multiple views. However, possible noise or redundancy between different views is ignored due to the idea of treating each view as equally important, resulting in poor final clustering performance. Therefore, an algorithm of adaptive weighted multi-view subspace clustering guided by manifold regularization(MR-AWMSC) is proposed in this paper. The consistent global low-rank representation information for each view is learned by nuclear norm, and difference information from different views is described by group effect. According to the concept of manifold regularization, the weight of each view is adaptively learned, and the contribution degree to the difference information of each view is automatically assigned. The difference information is integrated by the adaptive weight and the consistent information is fused to obtain the final consensus representation. The consensus representation is constructed for clustering multi-view data. Experimental results on six public datasets demonstrate that MR-AWMSC effectively improves the multi-view clustering performance.
[1] KUMAR A, DAUME III H.A Co-training Approach for Multi-view Spectral Clustering // Proc of the 28th International Conference on Machine Learning. Madison, USA: Omnipress, 2011: 393-400. [2] GUO D Y, ZHANG J, LIU X W, et al. Multiple Kernel Learning Based Multi-view Spectral Clustering // Proc of the 22nd International Conference on Pattern Recognition. Washington, USA: IEEE, 2014: 3774-3779. [3] ZHAN K, ZHANG C Q, GUAN J P, et al. Graph Learning for Mul-tiview Clustering. IEEE Transactions on Cybernetics, 2018, 48(10): 2887-2895. [4] GAO H C, NIE F P, LI X L, et al. Multi-view Subspace Clustering // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 4238-4246. [5] NIE F P, LI J, LI X L.Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification // Proc of the 25th International Joint Confe-rences on Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 2016: 1881-1887. [6] HAJJAR S E, DORNAIKA F, ABDALLAH F.One-Step Multi-view Spectral Clustering with Cluster Label Correlation Graph. Information Sciences, 2022, 592: 97-111. [7] SI X M, YIN Q Y, ZHAO X J, et al. Consistent and Diverse Multi-view Subspace Clustering with Structure Constraint. Pattern Recognition, 2022, 121. DOI: 10.1016/j.patcog.2021.108196. [8] CHEN Y Y, WANG S Q, ZHENG F Y, et al. Graph-Regularized Least Squares Regression for Multi-view Subspace Clustering. Knowledge-Based Systems, 2020, 194. DOI: 10.1016/j.knosys.2020.105482. [9] WANG Q P, CHEN X Y, CHEN W J. Auto-Weighted Graph Regularization and Residual Compensation for Multi-view Subspace Clustering[J/OL]. [2022-05-10]. https://doi.org/10.1007/s11063-022-10789-7. [10] 曹容玮,祝继华,郝问裕,等.双加权多视角子空间聚类算法.软件学报, 2022, 33(2): 585-597. (CAO R W, ZHU J H, HAO W Y, et al. Dual Weighted Multi-view Subspace Clustering. Journal of Software, 2022, 33(2): 585-597.) [11] 胡世哲,娄铮铮,王若彬,等.一种双重加权的多视角聚类算法.计算机学报, 2020, 43(9): 1708-1720. (HU S Z, LOU Z Z, WANG R B, et al. Dual-Weighted Multi-view Clustering. Chinese Journal of Computers, 2020, 43(9): 1708-1720.) [12] ELHAMIFAR E, VIDAL R.Sparse Subspace Clustering: Algorithm, Theory, and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2765-2781. [13] LIU G C, LIN Z C, YAN S C, et al. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pa-ttern Analysis and Machine Intelligence, 2013, 35(1): 171-184. [14] NIE F P, WANG X Q, HUANG H.Clustering and Projected Clustering with Adaptive Neighbors // Proc of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2014: 977-986. [15] BOYD S, PARIKH N, CHU E, et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Foundations and Trends® in Machine Learning, 2011, 3(1): 1-122. [16] CAI J F, CANDÈS E J, SHEN Z W. A Singular Value Thresholding Algorithm for Matrix Completion. SIAM Journal on Optimization, 2010, 20(4): 1956-1982. [17] YANG J F, YIN W T, ZHANG Y, et al. A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration. SIAM Journal on Imaging Sciences, 2009, 2(2): 569-592. [18] FU L L, LIN P F, VASILAKOS A V, et al. An Overview of Recent Multi-view Clustering. Neurocomputing, 2020, 402: 148-161. [19] ZHENG Q H, ZHU J H, LI Z Y, et al. Feature Concatenation Multi-view Subspace Clustering. Neurocomputing, 2020, 379: 89-102. [20] CHEN Y Y, XIAO X L, PENG C, et al. Low-Rank Tensor Graph Learning for Multi-view Subspace Clustering. IEEE Transactions on Circuits and System for Video Technology, 2022, 32(1): 92-104. [21] WANG X B, GUO X J, LEI Z, et al. Exclusivity-Consistency Re-gularized Multi-view Subspace Clustering // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017. DOI: 10.1109/CVPR.2017.8. [22] CAO X C, ZHANG C Q, FU H Z, et al. Diversity-Induced Multi-view Subspace Clustering // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 586-594. [23] ZHANG C Q, HU Q H, FU H Z, et al. Latent Multi-view Subspace Clustering // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 4333-4341. [24] LUO S R, ZHANG C Q, ZHANG W, et al. Consistent and Speci-fic Multi-view Subspace Clustering // Proc of the 32nd AAAI Conference on Artificial Intelligence and 30th Innovative Applications of Artificial Intelligence Conference and 8th AAAI Symposium on Educational Advances in Artificial Intelligence. Palo Alto, USA: AAAI, 2018: 3730-3737. [25] WANG H, YANG Y, LIU B.GMC: Graph-Based Multi-view Clu-stering. IEEE Transactions on Knowledge and Data Engineering, 2020, 32(6): 1116-1129. [26] ZHOU S H, LIU X W, LI M M, et al. Multiple Kernel Clustering with Neighbor-Kernel Subspace Segmentation. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(4): 1351-1362. [27] WANG S W, LIU X W, ZHU E, et al. Multi-view Clustering via Late Fusion Alignment Maximization // Proc of the 28th International Joint Conference on Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 2019: 3778-3784.
2022, Vol. 35 
