Zhu W, Nie F, Li X. Fast spectral clustering with efficient large graph construction. In: 2017 IEEE International conference on acoustics, speech and signal processing (ICASSP). 2017. pp 2492–2496. https://doi.org/10.1109/ICASSP.2017.7952605.
Huang D, Wang C-D, Peng H, Lai J, Kwoh C-K. Enhanced ensemble clustering via fast propagation of cluster-wise similarities. IEEE Trans Syst Man Cybern Syst. 2021;51(1):508–20.
Guo W, Shi Y, Wang S. A unified scheme for distance metric learning and clustering via rank-reduced regression. IEEE Trans Syst Man Cybern Syst. 2019; 1–12.
El Hajjar S, Dornaika F, Abdallah F. Multi-view spectral clustering via constrained nonnegative embedding. Inf Fusion. 2021.
Zhang X, Zheng Z, Gao D, et al. Multi-view consistent generative adversarial networks for compositional 3d-aware image synthesis. Int J Comput Vis. 2023;131(11):2219–42. https://doi.org/10.1007/s11263-023-01805-x.
Paul D, Chakdar D, Saha S, Mathew J. Multiview deep online clustering: an application to online research topic modeling and recommendations. IEEE Trans Comput Soc Syst. 2023;10(5):2566–78. https://doi.org/10.1109/TCSS.2022.3187342.
Tang K, Xu K, Su Z, Zhang N. Multi-view subspace clustering via consistent and diverse deep latent representations. Inf Sci. 2023;651. https://doi.org/10.1016/j.ins.2023.119719.
Sharma KK, Seal A. Multi-view spectral clustering for uncertain objects. Inform Sci. 2021;547:723–45.
Article MathSciNet MATH Google Scholar
Cheng D, Huang J, Zhang S, Zhang X, Luo X. A novel approximate spectral clustering algorithm with dense cores and density peaks. IEEE Trans Syst Man Cybern Syst. 2021.
Sharma KK, Seal A, Herrera-Viedma E, Krejcar O. An enhanced spectral clustering algorithm with s-distance. Symmetry. 2021;13(4):596.
Sellami L, Alaya B. SAMNET: Self-adaptative multi-kernel clustering algorithm for urban VANETs. Veh Commun. 2021;29:100332.
Ren Z, Yang SX, Sun Q, Wang T. Consensus affinity graph learning for multiple kernel clustering. IEEE Trans Cybern. 2020;51(6):3273–84.
Ma J, Zhang Y, Zhang L. Discriminative subspace matrix factorization for multiview data clustering. Pattern Recogn. 2021;111:107676.
Peng C, Zhang Z, Kang Z, Chen C, Cheng Q. Nonnegative matrix factorization with local similarity learning. Inf Sci. 2021;562:325–46.
Article MathSciNet MATH Google Scholar
Ren Z, Lei H, Sun Q, Yang C. Simultaneous learning coefficient matrix and affinity graph for multiple kernel clustering. Inf Sci. 2021;547:289–306.
Article MathSciNet MATH Google Scholar
Hu Z, Nie F, Wang R, Li X. Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding. Inf Fusion. 2020;55:251–9.
Von Luxburg U. A tutorial on spectral clustering. Stat Comput. 2007;17(4):395–416.
Article MathSciNet MATH Google Scholar
Kumar A, Daumé H. A co-training approach for multi-view spectral clustering. In: Proceedings of the 28th International conference on machine learning. ICML’11. Madison, WI, USA; 2011. pp. 393–400.
Kumar A, Daumé H. A co-training approach for multi-view spectral clustering. Proceedings of the 28th international conference on machine learning (ICML-11). 2011. pp. 393–400.
Tzortzis G, Likas A. Kernel-based weighted multi-view clustering. In: 2012 IEEE 12th International conference on data mining. IEEE; 2012. pp. 675–684.
Xu Y-M, Wang C-D, Lai J-H. Weighted multi-view clustering with feature selection. Pattern Recogn. 2016;53:25–35.
Huang Z, Ren Y, Pu X, Pan L, Yao D, Yu G. Dual self-paced multi-view clustering. Neural Netw. 2021;140:184–92.
Huang S, Kang Z, Xu Z. Auto-weighted multi-view clustering via deep matrix decomposition. Pattern Recogn. 2020;97:107015.
Zhu X, Zhang S, Zhu Y, Zheng W, Yang Y. Self-weighted multi-view fuzzy clustering. ACM Trans Knowl Discov Data (TKDD). 2020;14(4):1–17.
Wu Z, Liu S, Ding C, Ren Z, Xie S. Learning graph similarity with large spectral gap. IEEE Trans Syst Man Cybern Syst. 2019.
Cao X, Zhang C, Fu H, Liu S, Zhang H. Diversity-induced multi-view subspace clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. pp. 586–594.
White M, Yu Y, Zhang X, Schuurmans D. Convex multi-view subspace learning. In: Nips. Lake Tahoe, Nevada; 2012. pp. 1682–1690.
Wang Q, He X, Jiang X, Li X. Robust bi-stochastic graph regularized matrix factorization for data clustering. IEEE Trans Pattern Anal Mach Intell (2020)
Greene D, Cunningham P. A matrix factorization approach for integrating multiple data views. In: Joint European conference on machine learning and knowledge discovery in databases. Springer; 2009. pp. 423–438.
Yang Z, Liang N, Yan W, Li Z, Xie S. Uniform distribution non-negative matrix factorization for multiview clustering. IEEE Trans Cybern. 2020. pp. 1–14.
Horie M, Kasai H. Consistency-aware and inconsistency-aware graph-based multi-view clustering. In: 2020 28th European signal processing conference (EUSIPCO). IEEE; 2021. pp. 1472–1476.
Chen M-S, Huang L, Wang C-D, Huang D. Multi-view clustering in latent embedding space. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34. 2020. pp. 3513–3520.
Yang X, Zhu T, Wu D, Wang P, Liu Y, Nie F. Bidirectional fusion with cross-view graph filter for multi-view clustering. IEEE Trans Knowl Data Eng. 2024;1–6. https://doi.org/10.1109/TKDE.2024.3413682.
Yang J, Parikh D, Batra D. Joint unsupervised learning of deep representations and image clusters. Proc IEEE Conf Comput Vis Pattern Recognit. 2016; 5147–5156.
Zhan K, Nie F, Wang J, Yang Y. Multiview consensus graph clustering. IEEE Trans Image Process. 2019;28(3):1261–70. https://doi.org/10.1109/TIP.2018.2877335. Epub 2018 Oct 22 PMID: 30346283.
Yang Z, Tan Y. The methods for improving large-scale multi-view clustering efficiency: a survey. Artif Intell Rev. 2024;57(6):153. https://doi.org/10.1007/s10462-024-10785-4.
Zhou T, Zhang C, Peng X, Bhaskar H, Yang J. Dual shared-specific multiview subspace clustering. IEEE Trans Cybern. 2019;50(8):3517–30.
Dornaika F, El Hajjar S. Towards a unified framework for graph-based multi-view clustering. Neural Netw. 2024;173:106197. https://doi.org/10.1016/j.neunet.2024.106197.
Li Z, Melograna F, Hoskens H, Duroux D, Marazita ML, Walsh S, Weinberg SM, et al. netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity. Front Genet. 2023;14:1286800. https://doi.org/10.3389/fgene.2023.1286800.
Yan W, Zhang Y, Lv C, Tang C, Yue G, Liao L, Lin W. GCFAgg: Global and cross-view feature aggregation for multi-view clustering. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. 19863–19872.
Zahir A, Jbilou K, Ratnani A. High-dimensional multi-view clustering methods. arXiv:2303.08582. 2023.
Hu Z, Nie F, Chang W, Hao S, Wang R, Li X. Multi-view spectral clustering via sparse graph learning. Neurocomputing. 2020;384:1–10.
El Hajjar S, Dornaika F, Abdallah F. One-step multi-view spectral clustering with cluster label correlation graph. Inf Sci. 2022
El Hajjar S, Dornaika F, Abdallah F, Barrena N. Consensus graph and spectral representation for one-step multi-view kernel based clustering. Knowl-Based Syst. 2022;1:108250.
Dornaika F, El Hajjar S. Direct multi-view spectral clustering with consistent kernelized graph and convolved nonnegative representation. Artif Intell Rev. 2023;56:10987–1015.
Huang S, Kang Z, Tsang IW, Xu Z. Auto-weighted multi-view clustering via kernelized graph learning. Pattern Recogn. 2019;88:174–84.
Nie F, Wang X, Jordan MI, Huang H. The constrained Laplacian rank algorithm for graph-based clustering. In: AAAI. 2016. pp. 1969–1976.
Zhu X, Zhang S, He W, Hu R, Lei C, Zhu P. One-step multi-view spectral clustering. IEEE Trans Knowl Data Eng. 2019;31(10):2022–34. https://doi.org/10.1109/TKDE.2018.2873378.
Ren Z, Sun Q. Simultaneous global and local graph structure preserving for multiple kernel clustering. IEEE Trans Neural Netw Learn Syst. 2021;32(5):1839–51. https://doi.org/10.1109/TNNLS.2020.2991366.
Nie F, Cai G, Li X. Multi-view clustering and semi-supervised classification with adaptive neighbours. In: Thirty-First AAAI conference on artificial intelligence. 2017.
Nie F, Li J, Li X et al. Self-weighted multiview clustering with multiple graphs. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence (IJCAI-17). 2017.
Nie F, Tian L, Li X. Multiview clustering via adaptively weighted procrustes. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018. pp. 2022–2030.
Huang H-C, Chuang Y-Y, Chen C-S. Affinity aggregation for spectral clustering. In: 2012 IEEE Conference on computer vision and pattern recognition. IEEE; 2012. pp. 773–780.
Zhan K, Zhang C, Guan J, Wang J. Graph learning for multiview clustering. IEEE Trans Cybern. 2017;48(10):2887–95.
Nie F, Li J, Li X, et al. Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In: IJCAI. 2016: pp. 1881–1887.
El Hajjar S, Dornaika F, Abdallah F, Omrani H. Multi-view spectral clustering via integrating label and data graph learning. In: International conference on image analysis and processing. Springer; 2022. pp. 109–120.
Zhan K, Nie F, Wang J, Yang Y. Multiview consensus graph clustering. IEEE Trans Image Process. 2019;28(3):1261–70.
Article MathSciNet MATH Google Scholar
Van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(11).
留言 (0)