Concept Factorization-Based Collaborative Multi-view Clustering Algorithm in Visible and Latent Spaces
HU Suting1, SHEN Zongxin1, HUANG Qianqian2,3, HUANG Yanyong1
1. School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130; 2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756; 3. School of Computer Science and Technology, Southwest Minzu University, Chengdu 610041
Abstract：Multi-view clustering effectively improves the clustering performance by integrating the features derived from different views. The existing multi-view clustering methods more focus on different low-dimensional representations of data and their geometrical structures in latent space, while ignoring the structural relations of data in different spaces and the clustering of different spaces. To address this issue, a concept factorization-based collaborative multi-view clustering algorithm in visible and latent spaces is proposed in this paper. Firstly, common low-dimensional feature representation of different views in latent space is extracted through concept factorization. Besides, the local structure of the original data is preserved by means of graph Laplacian regularization. Then, the data clustering in visible and latent spaces are integrated into a unified framework for collaborative learning and optimizing to obtain the final clustering results. Experimental results on eight real datasets show the superiority of the proposed method.
[1] CLEUZIOU G, EXBRAYAT M, MARTIN L, et al. CoFKM: A Centralized Method for Multiple-View Clustering // Proc of the 9th IEEE International Conference on Data Mining. Washington USA: IEEE, 2009: 752-757. [2] KUMAR A, DAUMÉ III H.A Co-training Approach for Multi-view Spectral Clustering // Proc of the 28th International Conference on Machine Learning. New York USA: ACM, 2011: 393-400. [3] TZORTZIS G, LIKAS A.Kernel-Based Weighted Multi-view Clu-stering // Proc of the IEEE 12th International Conference on Data Mi-ning. Washington USA: IEEE, 2012: 675-684. [4] HUANG S D, KANG Z, TSANG I W, et al. Auto-Weighted Multi-view Clustering via Kernelized Graph Learning. Pattern Recognition, 2019, 88: 174-184. [5] REN Z W, LI H R, YANG C, et al. Multiple Kernel Subspace Clustering with Local Structural Graph and Low-Rank Consensus Kernel Learning. Knowledge-Based Systems, 2020, 188. DOI: 10.1016/j.knosys.2019.105040. [6] LEE D D, SEUNG H S. Algorithms for Non-negative Matrix Factorization // Proc of the 13th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2000: 535-541. [7] LIU J L, WANG C, GAO J, et al. Multi-view Clustering via Joint Nonnegative Matrix Factorization // Proc of the SIAM International Conference on Data Mining. Philadelphia USA: SIAM, 2013: 252-260. [8] TAN Y, OU W H, LONG F, et al. Multi-view Clustering via Co-regularized Nonnegative Matrix Factorization with Correlation Constraint // Proc of the 7th International Conference on Cloud Computing and Big Data. Washington USA: IEEE, 2016: 1-6. [9] ZONG L L, ZHANG X C, ZHAO L, et al. Multi-view Clustering via Multi-manifold Regularized Non-negative Matrix Factorization. Neural Networks, 2017, 88: 74-89. [10] LIU X Y, SONG P, SHENG C, et al. Robust Multi-view Non-ne-gative Matrix Factorization for Clustering. Digital Signal Processing, 2022, 123. DOI: 10.1016/j.dsp.2022.103447. [11] DENG Z H, LIU R X, XU P, et al. Multi-view Clustering with the Cooperation of Visible and Hidden Views. IEEE Transactions on Knowledge and Data Engineering, 2020, 34(2): 803-815. [12] CAI D, HE X F, HAN J W.Locally Consistent Concept Factorization for Document Clustering. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(6): 902-913. [13] XU W, GONG Y H.Document Clustering by Concept Factorization // Proc of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York USA: ACM, 2004: 202-209. [14] HINTON G E, ROWEIS S. Stochastic Neighbor Embedding // Proc of the 15th International Conference on Neural Information Proce-ssing Systems. Cambridge, USA: MIT Press, 2002: 857-864. [15] ZHAN K, SHI J H, WANG J, et al. Graph-Regularized Concept Factorization for Multi-view Document Clustering. Journal of Visual Communication and Image Representation, 2017, 48: 411-418. [16] CHANG S, HU J, LI T R, et al. Multi-view Clustering via Deep Concept Factorization. Knowledge-Based Systems, 2021, 217. DOI: 10.1016/j.knosys.2021.106807. [17] ZHAN K, SHI J H, WANG J, et al. Adaptive Structure Concept Factorization for Multi-view Clustering. Neural Computation, 2018, 30(4): 1080-1103. [18] LI R H, ZHANG C Q, HU Q H, et al. Flexible Multi-view Representation Learning for Subspace Clustering // Proc of the 28th International Joint Conference on Artificial Intelligence. San Francisco USA: IJCAI, 2019: 2916-2922. [19] YANG M S, SINAGA K P.Collaborative Feature-Weighted Multi-view Fuzzy C-means Clustering. Pattern Recognition, 2021, 119. DOI: 10.1016/j.patcog.2021.108064. [20] LIU J Y, LIU X W, YANG Y X, et al. Multiview Subspace Clustering via Co-training Robust Data Representation. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(10): 5177-5189. [21] ZHANG P, LIU X W, XIONG J, et al. Consensus One-Step Multi-view Subspace Clustering. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(10): 4676-4689. [22] WANG D, HAN S W, WANG Q, et al. Pseudo-Label Guided Co-llective Matrix Factorization for Multiview Clustering. IEEE Tran-sactions on Cybernetics, 2022, 52(9): 8681-8691.
2023, Vol. 36 
