research-article

Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance

Authors:

En ZhuAuthors Info & Claims

IEEE Transactions on Image Processing, Volume 31

Pages 556 - 568

https://doi.org/10.1109/TIP.2021.3131941

Published: 01 January 2022 Publication History

Abstract

Multi-view subspace clustering has attracted intensive attention to effectively fuse multi-view information by exploring appropriate graph structures. Although existing works have made impressive progress in clustering performance, most of them suffer from the cubic time complexity which could prevent them from being efficiently applied into large-scale applications. To improve the efficiency, anchor sampling mechanism has been proposed to select vital landmarks to represent the whole data. However, existing anchor selecting usually follows the heuristic sampling strategy, e.g. <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means or uniform sampling. As a result, the procedures of anchor selecting and subsequent subspace graph construction are separated from each other which may adversely affect clustering performance. Moreover, the involved hyper-parameters further limit the application of traditional algorithms. To address these issues, we propose a novel subspace clustering method termed Fast Parameter-free Multi-view Subspace Clustering with Consensus Anchor Guidance (FPMVS-CAG). Firstly, we jointly conduct anchor selection and subspace graph construction into a unified optimization formulation. By this way, the two processes can be negotiated with each other to promote clustering quality. Moreover, our proposed FPMVS-CAG is proved to have linear time complexity with respect to the sample number. In addition, FPMVS-CAG can automatically learn an optimal anchor subspace graph without any extra hyper-parameters. Extensive experimental results on various benchmark datasets demonstrate the effectiveness and efficiency of the proposed method against the existing state-of-the-art multi-view subspace clustering competitors. These merits make FPMVS-CAG more suitable for large-scale subspace clustering. The code of FPMVS-CAG is publicly available at <uri>https://github.com/wangsiwei2010/FPMVS-CAG</uri>.

References

[1]

Y. Wang, X. Lin, L. Wu, W. Zhang, Q. Zhang, and X. Huang, “Robust subspace clustering for multi-view data by exploiting correlation consensus,” IEEE Trans. Image Process., vol. 24, no. 11, pp. 3939–3949, Nov. 2015.

Digital Library

[2]

E. Elhamifar and R. Vidal, “Sparse subspace clustering: Algorithm, theory, and applications,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 11, pp. 2765–2781, Nov. 2013.

Digital Library

[3]

F. Nie, G. Cai, and X. Li, “Multi-view clustering and semi-supervised classification with adaptive neighbours,” in Proc. AAAI Conf. Artif. Intell., 2017, vol. 31, no. 1, pp. 2408–2414.

[4]

Z. Zhang, F. Li, M. Zhao, L. Zhang, and S. Yan, “Robust neighborhood preserving projection by nuclear/L2,1-norm regularization for image feature extraction,” IEEE Trans. Image Process., vol. 26, no. 4, pp. 1607–1622, Apr. 2017.

Digital Library

[5]

X. Peng, Z. Huang, J. Lv, H. Zhu, and J. T. Zhou, “COMIC: Multi-view clustering without parameter selection,” in Proc. Int. Conf. Mach. Learn., 2019, pp. 5092–5101.

[6]

X. Liu, X. Zhu, M. Li, L. Wang, C. Tang, J. Yin, D. Shen, H. Wang, and W. Gao, “Late fusion incomplete multi-view clustering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 10, pp. 2410–2423, Oct. 2019.

Digital Library

[7]

Q. Gao, W. Xia, Z. Wan, D. Xie, and P. Zhang, “Tensor-SVD based graph learning for multi-view subspace clustering,” in Proc. AAAI Conf. Artif. Intell., 2020, vol. 34, no. 4, pp. 3930–3937.

[8]

Y. Li, F. Nie, H. Huang, and J. Huang, “Large-scale multi-view spectral clustering via bipartite graph,” in Proc. 2nd AAAI Conf. Artif. Intell., 2015, pp. 2750–2756.

[9]

C. Zhanget al., “Generalized latent multi-view subspace clustering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 1, pp. 86–99, Jan. 2020.

Digital Library

[10]

R. Xia, Y. Pan, L. Du, and J. Yin, “Robust multi-view spectral clustering via low-rank and sparse decomposition,” in Proc. 28th AAAI Conf. Artif. Intell., 2014, pp. 2149–2155.

[11]

W. Lianget al., “Multi-view spectral clustering with high-order optimal neighborhood Laplacian matrix,” IEEE Trans. Knowl. Data Eng., early access, Sep. 18, 2020. 10.1109/TKDE.2020.3025100.

[12]

X. Li, H. Zhang, R. Wang, and F. Nie, “Multi-view clustering: A scalable and parameter-free bipartite graph fusion method,” IEEE Trans. Pattern Anal. Mach. Intell., early access, Jul. 22, 2020. 10.1109/TPAMI.2020.3011148.

[13]

M. Sunet al., “Projective multiple kernel subspace clustering,” IEEE Trans. Multimedia, early access, Jun. 4, 2021. 10.1109/TMM.2021.3086727.

[14]

Q. Ou, S. Wang, S. Zhou, M. Li, X. Guo, and E. Zhu, “Anchor-based multiview subspace clustering with diversity regularization,” IEEE MultimediaMag., vol. 27, no. 4, pp. 91–101, Oct. 2020.

Digital Library

[15]

C. Zhang, H. Fu, S. Liu, G. Liu, and X. Cao, “Low-rank tensor constrained multiview subspace clustering,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Dec. 2015, pp. 1582–1590.

[16]

X. Wang, X. Guo, Z. Lei, C. Zhang, and S. Z. Li, “Exclusivity-consistency regularized multi-view subspace clustering,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 923–931.

[17]

C. Zhang, H. Fu, J. Wang, W. Li, X. Cao, and Q. Hu, “Tensorized multi-view subspace representation learning,” Int. J. Comput. Vis., vol. 128, no. 8, pp. 2344–2361, 2020.

Digital Library

[18]

F. Nie, G. Cai, J. Li, and X. Li, “Auto-weighted multi-view learning for image clustering and semi-supervised classification,” IEEE Trans. Image Process., vol. 27, no. 3, pp. 1501–1511, Mar. 2018.

[19]

S. Wang, X. Liu, L. Liu, S. Zhou, and E. Zhu, “Late fusion multiple kernel clustering with proxy graph refinement,” IEEE Trans. Neural Netw. Learn. Syst., early access, Oct. 14, 2021. 10.1109/TNNLS.2021.3117403.

[20]

C. Zhanget al., “Multi-view clustering via deep matrix factorization and partition alignment,” in Proc. 29th ACM Int. Conf. Multimedia, Oct. 2021, pp. 4156–4164.

[21]

X. Yao, J. Han, D. Zhang, and F. Nie, “Revisiting co-saliency detection: A novel approach based on two-stage multi-view spectral rotation co-clustering,” IEEE Trans. Image Process., vol. 26, no. 7, pp. 3196–3209, Jul. 2017.

Digital Library

[22]

J. Xu, J. Han, F. Nie, and X. Li, “Re-weighted discriminatively embedded K-means for multi-view clustering,” IEEE Trans. Image Process., vol. 26, no. 6, pp. 3016–3027, Jun. 2017.

Digital Library

[23]

K. Zhan, F. Nie, J. Wang, and Y. Yang, “Multiview consensus graph clustering,” IEEE Trans. Image Process., vol. 28, no. 3, pp. 1261–1270, Mar. 2019.

Digital Library

[24]

Z. Kang, W. Zhou, Z. Zhao, J. Shao, M. Han, and Z. Xu, “Large-scale multi-view subspace clustering in linear time,” in Proc. AAAI Conf. Artif. Intell., 2020, vol. 34, no. 4, pp. 4412–4419.

[25]

X. Peng, J. Feng, S. Xiao, W.-Y. Yau, J. T. Zhou, and S. Yang, “Structured autoencoders for subspace clustering,” IEEE Trans. Image Process., vol. 27, no. 10, pp. 5076–5086, Oct. 2018.

[26]

M. Sunet al., “Scalable multi-view subspace clustering with unified anchors,” in Proc. 29th ACM Int. Conf. Multimedia, Oct. 2021, pp. 3528–3536.

[27]

X. Liuet al., “One pass late fusion multi-view clustering,” in Proc. Int. Conf. Mach. Learn., 2021, pp. 6850–6859.

[28]

X. Liuet al., “Absent multiple kernel learning algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 6, pp. 1303–1316, Jun. 2020.

[29]

X. Liuet al., “Efficient and effective regularized incomplete multi-view clustering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 8, pp. 2634–2646, Aug. 2021.

[30]

C. Zhang, Q. Hu, H. Fu, P. Zhu, and X. Cao, “Latent multi-view subspace clustering,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 4279–4287.

[31]

P. Zhanget al., “Consensus one-step multi-view subspace clustering,” IEEE Trans. Knowl. Data Eng., early access, Dec. 18, 2020. 10.1109/TKDE.2020.3045770.

[32]

F. Nieet al., “Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification,” in Proc. IJCAI, 2016, pp. 1881–1887.

[33]

X. Wang, Z. Lei, X. Guo, C. Zhang, H. Shi, and S. Z. Li, “Multi-view subspace clustering with intactness-aware similarity,” Pattern Recognit., vol. 88, pp. 50–63, Apr. 2019.

Digital Library

[34]

Y. Wang and L. Wu, “Beyond low-rank representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering,” Neural Netw., vol. 103, pp. 1–8, Jul. 2018.

Digital Library

[35]

H. Wang, F. Nie, and H. Huang, “Multi-view clustering and feature learning via structured sparsity,” in Proc. Int. Conf. Mach. Learn., 2013, pp. 352–360.

[36]

H. Gao, F. Nie, X. Li, and H. Huang, “Multi-view subspace clustering,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Dec. 2015, pp. 4238–4246.

[37]

X. Cao, C. Zhang, H. Fu, S. Liu, and H. Zhang, “Diversity-induced multi-view subspace clustering,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2015, pp. 586–594.

[38]

S. Luo, C. Zhang, W. Zhang, and X. Cao, “Consistent and specific multi-view subspace clustering,” in Proc. AAAI Conf. Artif. Intell., 2018, vol. 32, no. 1, pp. 3731–3737.

[39]

M. Brbić and I. Kopriva, “Multi-view low-rank sparse subspace clustering,” Pattern Recognit., vol. 73, pp. 247–258, Jan. 2018.

[40]

Z. Kanget al., “Partition level multiview subspace clustering,” Neural Netw., vol. 122, pp. 279–288, Feb. 2020.

Digital Library

[41]

X. Peng, H. Tang, L. Zhang, Z. Yi, and S. Xiao, “A unified framework for representation-based subspace clustering of out-of-sample and large-scale data,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 12, pp. 2499–2512, Dec. 2016.

[42]

X. Liuet al., “Multiple kernel KK-means with incomplete kernels,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 5, pp. 1191–1204, May 2020.

[43]

S. Wanget al., “Multi-view clustering via late fusion alignment maximization,” in Proc. 28th Int. Joint Conf. Artif. Intell., Aug. 2019, pp. 3778–3784.

[44]

L. Fei-Fei, R. Fergus, and P. Perona, “Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories,” in Proc. Conf. Comput. Vis. Pattern Recognit. Workshop, 2004, p. 178.

[45]

S. Song, S. P. Lichtenberg, and J. Xiao, “SUN RGB-D: A RGB-D scene understanding benchmark suite,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2015, pp. 567–576.

[46]

T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y.-T. Zheng, “NUS-WIDE: A real-world web image database from National University of Singapore,” in Proc. ACM Int. Conf. Image Video Retr. (CIVR), Santorini, Greece, Jul. 2009, pp. 1–9.

[47]

M.-S. Chen, L. Huang, C.-D. Wang, and D. Huang, “Multi-view clustering in latent embedding space,” in Proc. AAAI Conf. Artif. Intell., 2020, vol. 34, no. 4, pp. 3513–3520.

[48]

R. Li, C. Zhang, Q. Hu, P. Zhu, and Z. Wang, “Flexible multi-view representation learning for subspace clustering,” in Proc. 28th Int. Joint Conf. Artif. Intell., Aug. 2019, pp. 2916–2922.

[49]

Z. Kanget al., “Multiple partitions aligned clustering,” in Proc. 28th Int. Joint Conf. Artif. Intell., Aug. 2019, pp. 2701–2707.

[50]

X. Cai, F. Nie, and H. Huang, “Multi-view K-means clustering on big data,” in Proc. 23rd Int. Joint Conf. Artif. Intell., 2013, pp. 2598–2604.

[51]

Z. Zhang, L. Liu, F. Shen, H. T. Shen, and L. Shao, “Binary multi-view clustering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 7, pp. 1774–1782, Jul. 2018.

Digital Library

[52]

J. C. Bezdek and R. J. Hathaway, “Convergence of alternating optimization,” Neural, Parallel Sci. Comput., vol. 11, no. 4, pp. 351–368, 2003.

Digital Library

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cover image IEEE Transactions on Image Processing

IEEE Transactions on Image Processing Volume 31, Issue

2022

3518 pages

ISSN:1057-7149

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1941-0042 © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 January 2022

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Cited By

Xiang L(2024)Scalable Multi-view Spectral Clustering Based on Spectral Perturbation TheoryProceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674434(92-99)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3674399.3674434
Gu ZFeng SLi ZYuan JLiu J(2024)NOODLE: Joint Cross-View Discrepancy Discovery and High-Order Correlation Detection for Multi-View Subspace ClusteringACM Transactions on Knowledge Discovery from Data10.1145/365330518:6(1-23)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3653305
Zhou LDu GLü KWang LDu J(2024)A Survey and an Empirical Evaluation of Multi-View Clustering ApproachesACM Computing Surveys10.1145/364510856:7(1-38)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3645108
Li JGao QWang QDeng CXie DBaeza-Yates RBonchi F(2024)Label Learning Method Based on Tensor ProjectionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671671(1599-1609)Online publication date: 25-Aug-2024
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Zhao WLi QXu HGao QWang QGao X(2024)Anchor Graph-Based Feature Selection for One-Step Multi-View ClusteringIEEE Transactions on Multimedia10.1109/TMM.2024.336760526(7413-7425)Online publication date: 26-Feb-2024
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Qin YPu NWu H(2024)EDMC: Efficient Multi-View Clustering via Cluster and Instance Space LearningIEEE Transactions on Multimedia10.1109/TMM.2023.333119726(5273-5283)Online publication date: 1-Jan-2024
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Long ZZhu CChen JLi ZRen YLiu Y(2024)Multi-View MERA Subspace ClusteringIEEE Transactions on Multimedia10.1109/TMM.2023.330723926(3102-3112)Online publication date: 1-Jan-2024
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Liu SLiao QWang SLiu XZhu E(2024)Robust and Consistent Anchor Graph Learning for Multi-View ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336466336:8(4207-4219)Online publication date: 1-Aug-2024
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