research-article

Efficient Multi-View -Means for Image Clustering<italic/>

Authors:

Xinbo GaoAuthors Info & Claims

IEEE Transactions on Image Processing, Volume 33

Pages 273 - 284

https://doi.org/10.1109/TIP.2023.3340609

Published: 13 December 2023 Publication History

Abstract

Nowadays, data in the real world often comes from multiple sources, but most existing multi-view <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula>-Means perform poorly on linearly non-separable data and require initializing the cluster centers and calculating the mean, which causes the results to be unstable and sensitive to outliers. This paper proposes an efficient multi-view <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula>-Means to solve the above-mentioned issues. Specifically, our model avoids the initialization and computation of clusters centroid of data. Additionally, our model use the Butterworth filters function to transform the adjacency matrix into a distance matrix, which makes the model is capable of handling linearly inseparable data and insensitive to outliers. To exploit the consistency and complementarity across multiple views, our model constructs a third tensor composed of discrete index matrices of different views and minimizes the tensor’s rank by tensor Schatten <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-norm. Experiments on two artificial datasets verify the superiority of our model on linearly inseparable data, and experiments on several benchmark datasets illustrate the performance.

References

[1]

S. Bickel and T. Scheffer, “Multi-view clustering,” in Proc. ICDM, 2004, pp. 19–26.

[2]

T. Xia, D. Tao, T. Mei, and Y. Zhang, “Multiview spectral embedding,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, no. 6, pp. 1438–1446, Dec. 2010.

Digital Library

[3]

C.-K. Lee and T.-L. Liu, “Guided co-training for multi-view spectral clustering,” in Proc. IEEE Int. Conf. Image Process. (ICIP), Sep. 2016, pp. 393–400.

[4]

K. Zhan, C. Zhang, J. Guan, and J. Wang, “Graph learning for multiview clustering,” IEEE Trans. Cybern., vol. 48, no. 10, pp. 2887–2895, Oct. 2018.

[5]

Q. Gao, W. Xia, Z. Wan, D. Xie, and P. Zhang, “Tensor-SVD based graph learning for multi-view subspace clustering,” in Proc. AAAI, 2020, pp. 3930–3937.

[6]

W. Xia, Q. Gao, Q. Wang, X. Gao, C. Ding, and D. Tao, “Tensorized bipartite graph learning for multi-view clustering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 4, pp. 5187–5202, Apr. 2023.

Digital Library

[7]

H. Yang, Q. Gao, W. Xia, M. Yang, and X. Gao, “Multiview spectral clustering with bipartite graph,” IEEE Trans. Image Process., vol. 31, pp. 3591–3605, 2022.

[8]

Q. Wang, Z. Ding, Z. Tao, Q. Gao, and Y. Fu, “Generative partial multi-view clustering with adaptive fusion and cycle consistency,” IEEE Trans. Image Process., vol. 30, pp. 1771–1783, 2021.

[9]

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

[10]

B. Yang, X. Zhang, F. Nie, and F. Wang, “Fast multiview clustering with spectral embedding,” IEEE Trans. Image Process., vol. 31, pp. 3884–3895, 2022.

Digital Library

[11]

Y. Chen, X. Xiao, C. Peng, G. Lu, and Y. Zhou, “Low-rank tensor graph learning for multi-view subspace clustering,” IEEE Trans. Circuits Syst. Video Technol., vol. 32, no. 1, pp. 92–104, Jan. 2022.

Digital Library

[12]

B. Yang, X. Zhang, Z. Lin, F. Nie, B. Chen, and F. Wang, “Efficient and robust MultiView clustering with anchor graph regularization,” IEEE Trans. Circuits Syst. Video Technol., vol. 32, no. 9, pp. 6200–6213, Sep. 2022.

[13]

J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. Berkeley Symp. Math. Statist. Probab., vol. 1, no. 14, 1965, pp. 281–297.

[14]

D. Arthur and S. Vassilvitskii, “k-means++: The advantages of careful seeding,” in Proc. SODA, 2007, pp. 1027–1035.

[15]

S. Pei, H. Chen, F. Nie, R. Wang, and X. Li, “Centerless clustering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 1, pp. 167–181, Jan. 2023.

[16]

D.-W. Kim, K. Y. Lee, D. Lee, and K. H. Lee, “Evaluation of the performance of clustering algorithms in kernel-induced feature space,” Pattern Recognit., vol. 38, no. 4, pp. 607–611, Apr. 2005.

Digital Library

[17]

R. Wang, J. Lu, Y. Lu, F. Nie, and X. Li, “Discrete and parameter-free multiple kernel k-Means,” IEEE Trans. Image Process., vol. 31, pp. 2796–2808, 2022.

[18]

L. He and H. Zhang, “Kernel k-means sampling for Nyström approximation,” IEEE Trans. Image Process., vol. 27, no. 5, pp. 2108–2120, May 2018.

[19]

Z. Ren, Q. Sun, and D. Wei, “Multiple kernel clustering with kernel k-means coupled graph tensor learning,” in Proc. AAAI, 2021, pp. 9411–9418.

[20]

J. Ren, K. Hua, and Y. Cao, “Global optimal k-medoids clustering of one million samples,” in Proc. NeurIPS, 2022, pp. 982–994.

[21]

J. Newling and F. Fleuret, “K-medoids for k-means seeding,” in Proc. NeurIPS, 2017, pp. 5195–5203.

[22]

X. Cai, F. Nie, and H. Huang, “Multi-view k-means clustering on big data,” in Proc. IJCAI, 2013, pp. 2598–2604.

[23]

J. Han, J. Xu, F. Nie, and X. Li, “Multi-view K-means clustering with adaptive sparse memberships and weight allocation,” IEEE Trans. Knowl. Data Eng., vol. 34, no. 2, pp. 816–827, Feb. 2022.

Digital Library

[24]

C. H. Q. Ding, X. He, and H. D. Simon, “Nonnegative Lagrangian relaxation of K-means and spectral clustering,” in Proc. ECML, vol. 3720, 2005, pp. 530–538.

[25]

Q. Gao, P. Zhang, W. Xia, D. Xie, X. Gao, and D. Tao, “Enhanced tensor RPCA and its application,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 6, pp. 2133–2140, Jun. 2021.

[26]

W. Xia, X. Zhang, Q. Gao, X. Shu, J. Han, and X. Gao, “Multiview subspace clustering by an enhanced tensor nuclear norm,” IEEE Trans. Cybern., vol. 52, no. 9, pp. 8962–8975, Sep. 2022. 10.1109/TCYB.2021.3052352.

[27]

X. Li, H. Zhang, R. Wang, and F. Nie, “Multiview clustering: A scalable and parameter-free bipartite graph fusion method,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 1, pp. 330–344, Jan. 2022.

Digital Library

[28]

F. Nie, X. Wang, M. I. Jordan, and H. Huang, “The constrained Laplacian rank algorithm for graph-based clustering,” in Proc. AAAI, 2016, pp. 1969–1976.

[29]

W. Liu, J. He, and S. Chang, “Large graph construction for scalable semi-supervised learning,” in Proc. ICML, 2010, pp. 679–686.

[30]

Z. Lin, R. Liu, and Z. Su, “Linearized alternating direction method with adaptive penalty for low-rank representation,” in Proc. NeurIPS, 2011, pp. 612–620.

[31]

H. Xu, X. Zhang, W. Xia, Q. Gao, and X. Gao, “Low-rank tensor constrained co-regularized multi-view spectral clustering,” Neural Netw., vol. 132, pp. 245–252, Dec. 2020.

[32]

J. Winn and N. Jojic, “LOCUS: Learning object classes with unsupervised segmentation,” in Proc. 10th IEEE Int. Conf. Comput. Vis. (ICCV), Oct. 2005, pp. 756–763.

[33]

D. Dua and C. Graff, “UCI machine learning repository,” School Inf. Comput. Sci., Univ. California, Irvine, CA, USA, 2019. [Online]. Available: https://archive.ics.uci.edu/ml

[34]

S. Luo, C. Zhang, W. Zhang, and X. Cao, “Consistent and specific multi-view subspace clustering,” in Proc. AAAI, 2018, pp. 3730–3737.

[35]

L. Deng, “The MNIST database of handwritten digit images for machine learning research,” IEEE Signal Process. Mag., vol. 29, no. 6, pp. 141–142, 2012.

[36]

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

[37]

C. Apté, F. Damerau, and S. M. Weiss, “Automated learning of decision rules for text categorization,” ACM Trans. Inf. Syst., vol. 12, no. 3, pp. 233–251, Jul. 1994.

Digital Library

[38]

A. Y. Ng, M. I. Jordan, and Y. Weiss, “On spectral clustering: Analysis and an algorithm,” in Proc. NIPS, 2001, pp. 849–856.

[39]

A. Kumar, P. Rai, and H. D. III, “Co-regularized multi-view spectral clustering,” in Proc. NeurIPS, 2011, pp. 1413–1421.

[40]

Y.-M. Xu, C.-D. Wang, and J.-H. Lai, “Weighted multi-view clustering with feature selection,” Pattern Recognit., vol. 53, pp. 25–35, May 2016.

Digital Library

[41]

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

[42]

J. Wu, Z. Lin, and H. Zha, “Essential tensor learning for multi-view spectral clustering,” IEEE Trans. Image Process., vol. 28, no. 12, pp. 5910–5922, Dec. 2019.

[43]

M.-S. Yang and K. P. Sinaga, “A feature-reduction multi-view k-means clustering algorithm,” IEEE Access, vol. 7, pp. 114472–114486, 2019.

Cited By

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
https://dl.acm.org/doi/10.1145/3637528.3671671
Qin YQin CZhang XFeng G(2024)Dual Consensus Anchor Learning for Fast Multi-View ClusteringIEEE Transactions on Image Processing10.1109/TIP.2024.345965133(5298-5311)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3459651

Index Terms

Efficient Multi-View -Means for Image Clustering
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Image Processing

IEEE Transactions on Image Processing Volume 33, Issue

2024

6889 pages

ISSN:1057-7149

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1941-0042 © 2023 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: 13 December 2023

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Cited By

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
https://dl.acm.org/doi/10.1145/3637528.3671671
Qin YQin CZhang XFeng G(2024)Dual Consensus Anchor Learning for Fast Multi-View ClusteringIEEE Transactions on Image Processing10.1109/TIP.2024.345965133(5298-5311)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3459651

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