Abstract
Graph-based multi-view clustering has gained increasing attention due to its ability to effectively unveil complex nonlinear structures among data points from various views. Nevertheless, prior studies usually focus on amalgamating multiple similarity graphs derived from the initial data into a consensus one to serve the subsequent clustering task, leading to the clustering performance is notably contingent upon the inherent quality of the original features. Moreover, many prevailing approaches employ a two-phase methodology comprising graph construction followed by graph partitioning, which impedes the acquired graph from manifesting a structure conducive to the requirements of the clustering task. To overcome these issues, we propose an ONE-Step graph-based multi-view clustering via Early Fusion (ONESELF) method, which jointly conducts the robust latent representation extraction and the target structured graph construction into a cohesive optimization formulation. Specifically, a robust latent representation compatible across multiple views is firstly extracted from the original multiple features to mitigate the impact of inevitable noise and outliers. Subsequently, a consensus graph is formed by incorporating a connectivity constraint based on the latent representation, enabling direct extraction of clustering labels from the resulting graph without requiring further post-processing steps. Furthermore, we present an adept algorithm to efficiently optimize the objective function. Empirical results on seven benchmark datasets substantiate the superiority of our proposed method over several contemporary algorithms.





Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Macqueen J (1965) Some methods for classification and analysis of multivariate observations. In: Proceedings of berkeley symposium on mathematical statistics and probability, pp 281–297
Ng AY, Jordan MI, Weiss Y, (2002) On spectral clustering: analysis and an algorithm. In: Proceedings of the international conference on neural information processing systems, pp 849–856
Elhamifar E, Vidal R, (2009) Sparse subspace clustering. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition workshops, pp 2790–2797
Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. Proceedings of IEEE European conference on computer vision 3952:428–441
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Zhang C, Fu H, Hu Q, Cao X, Xie Y, Tao D, Xu D (2020) Generalized latent multi-view subspace clustering. IEEE Trans Pattern Anal Mach Intell 42(1):86–99
Fan R, Luo T, Zhuge W, Qiang S, Hou C (2020) Multi-view subspace learning via bidirectional sparsity. Pattern Recog 108:1–11
Xiao C, Nie F, Huang H, Kamangar F (2011) Heterogeneous image feature integration via multi-modal spectral clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1977–1984
Nie F, Jing L, Li X (2017) Self-weighted multiview clustering with multiple graphs. In: Proceedings of the international joint conference on artificial intelligence, pp 2564–2570
Li Y, Nie F, Huang H, Huang J (2015) Large-scale multi-view spectral clustering via bipartite graph. In: Proceedings of the AAAI conference on artificial intelligence, pp 2750–2756
Li X, Zhang H, Wang R, Nie F (2022) Multi-view clustering: a scalable and parameter-free bipartite graph fusion method. IEEE Trans Pattern Anal Mach Intell 44(1):330–344
Tang C, Liu X, Zhu X, Zhu E, Luo Z, Wang L, Gao W (2020) Cgd: multi-view clustering via cross-view graph diffusion. In: Proceedings of the AAAI conference on artificial intelligence, pp 5924–5931
Hu Y, Song Z, Wang B, Gao J, Sun Y, Yin B (2021) Akm3c: adaptive k-multiple-means for multi-view clustering. IEEE Trans Circ Syst Vid Technol 31(11):4214–4226
Yang B, Zhang X, Lin Z, Nie F, Chen B, Wang F (2022) Efficient and robust multiview clustering with anchor graph regularization. IEEE Trans Circ Syst Vid Technol 32(9):6200–6213
Jiang G, Peng J, Wang H, Mi Z, Fu X (2022) Tensorial multi-view clustering via low-rank constrained high-order graph learning. IEEE Trans Circ Syst Vid Technol 32(8):5307–5318
Liang W, Liu X, Zhou S, Liu J, Wang S, Zhu E (2022) Robust graph-based multi-view clustering. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 7462–7469
Kumar A, Iii HD (2011) A co-training approach for multi-view spectral clustering. In: Proceedings of international conference on international conference on machine learning, pp 393–400
Lee CK, Liu TL (2016) Guided co-training for multi-view spectral clustering. In: Proceedings of IEEE international conference on image processing, pp 4042–4046
Yu S, Tranchevent L, Liu X, Glanzel W, Suykens JAK, Moor BD, Moreau Y (2012) Optimized data fusion for kernel k-means clustering. IEEE Trans Pattern Anal Mach Intell 34(5):1031–1039
Zhou S, Liu X, Li M, Zhu E, Liu L, Zhang C, Yin J (2020) Multiple kernel clustering with neighbor-kernel subspace segmentation. IEEE Trans Neural Netw Learn Syst 31(4):1351–1362
Liu X, Wang L, Zhu X, Li M, Zhu E, Liu T, Liu L, Dou Y, Yin J (2020) Absent multiple kernel learning algorithms. IEEE Trans Pattern Anal Mach Intell 42(6):1303–1316
Liu X (2022) Simplemkkm: simple multiple kernel k-means. IEEE Trans Pattern Anal Mach Intell (01):1–13
Gao H, Nie F, Li X, Huang H (2016) Multi-view subspace clustering. In: Proceedings of IEEE international conference on computer vision, pp 4234–4246
Zhang C, Hu Q, Fu H, Zhu P, Cao X (2017) Latent multi-view subspace clustering. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 4333–4341
Chen MS, Huang L, Wang CD, Huang D (2020) Multi-view clustering in latent embedding space. Proceedings of the AAAI conference on artificial intelligence 34:3513–3520
Chen Y, Xiao X, Peng C, Lu G, Zhou Y (2022) Low-rank tensor graph learning for multi-view subspace clustering. IEEE Trans Circ Syst Vid Technol 32(1):92–104
Jia Y, Liu H, Hou J, Kwong S, Zhang Q (2021) Multi-view spectral clustering tailored tensor low-rank representation. IEEE Trans Circ Syst Vid Technol 31(12):4784–4797
Lan M, Meng M, Yu J, Wu J (2022) Generalized multi-view collaborative subspace clustering. IEEE Trans Circ Syst Vid Technol 32(6):3561–3574
Kumar A, Rai P, Daumé H (2011) Co-regularized multi-view spectral clustering. In: Proceedings of the international conference on neural information processing systems, pp 1413–1421
Nie F, Jing L, Li X (2016) Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In: Proceedings of the international joint conference on artificial intelligence, pp 1881–1887
Nie F, Cai G, Li X (2017) Multi-view clustering and semi-supervised classification with adaptive neighbours. In: Proceedings of the AAAI conference on artificial intelligence, pp 2408–2414
Hao W, Yan YA, Bing LB, Hf C (2019) A study of graph-based system for multi-view clustering. Knowl-Based Syst 163:1009–1019
Zhan K, Zhang C, Guan J, Wang J (2018) Graph learning for multiview clustering. IEEE Trans Cybern 48(10):2887–2895
Zhan K, Nie F, Wang J, Yang Y (2019) Multiview consensus graph clustering. IEEE Trans Image Process 28(3):1261–1270
Wang H, Yang Y, Liu B (2020) GMC: graph-based multi-view clustering. IEEE Trans Knowl Data Eng 32(6):1116–1129
Zhan K, Niu C, Chen C, Nie F, Zhang C, Yang Y (2019) Graph structure fusion for multiview clustering. IEEE Trans Knowl Data Eng 31(10):1984–1993
Xia R, Pan Y, Du L, Yin J (2014) Robust multi-view spectral clustering via low-rank and sparse decomposition. In: Proceedings of the AAAI conference on artificial intelligence, pp 2149–2155
Yu Z, Zhong Z, Yang K, Cao W, Chen CLP (2024) Broad learning autoencoder with graph structure for data clustering. IEEE Trans Knowl Data Eng 36(1):49–61
Shi Y, Yang K, Yu Z, Chen CLP, Zeng H (2023) Adaptive ensemble clustering with boosting bls-based autoencoder. IEEE Trans Knowl Data Eng 35(12):12369–12383
Yang S, Yu K, Cao F, Liu L, Wang H, Li J (2023) Learning causal representations for robust domain adaptation. IEEE Trans Knowl Data Eng 35(3):2750–2764
Nie F, Wang X, Huang H (2014) Clustering and projected clustering with adaptive neighbors. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining, pp 977–986
Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):220–227
Fan K (1949) On a theorem of weyl concerning eigenvalues of linear transformations I. Proceedings of the National Academy of Sciences of the United States of America 35(11):652–655
Gu S, Zhang L, Zuo W, Feng X (2014) Proceedings of projective dictionary pair learning for pattern classification. In: Advances in neural information processing systems pp 793–801
Bartels RH, Stewart GW (1972) Solution of the matrix equation AX + XB = C. Communications of the ACM 15(9):820–826
Huang S, Tsang I, Xu Z, Lv JC (2021) Measuring diversity in graph learning: a unified framework for structured multi-view clustering. IEEE Trans Knowledge Data Eng 14(8):1–14
Ikizler N, Cinbis RG, Pehlivan S, Duygulu P (2008) Recognizing actions from still images. In: Proceedings of the 19th international conference on pattern recognition 2008:1–4
Tang C, Liu X, Zhu X, Zhu E, Luo Z, Wang L, Gao W (2020b) CGD: multi-view clustering via cross-view graph diffusion pp 5924–5931
Huang S, Tsang IWH, Xu Z, Lv J, Liu QH (2022) Multi-view clustering on topological manifold. In: AAAI Conference on artificial intelligence 2022:7462–7469
Acknowledgements
This work was supported by the Beijing Natural Science Foundation (No.4242046), the Engineering Research Center of Integration and Application of Digital Learning Technology (No. 1321003), and the Fundamental Research Funds for the Central Universitie (JLU).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gu, Z., Feng, S., Yuan, J. et al. Consensus representation-driven structured graph learning for multi-view clustering. Appl Intell 54, 8545–8562 (2024). https://doi.org/10.1007/s10489-024-05616-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-024-05616-6