research-article
Authors: Xuemei Han, Fei Zhou, Zhenwen Ren, Xueyuan Wang, and Xiaojian You
Volume 664, Issue C
Published: 25 June 2024 Publication History
- 0citation
- 0
- Downloads
Metrics
Total Citations0Total Downloads0Last 12 Months0
Last 6 weeks0
New Citation Alert added!
This alert has been successfully added and will be sent to:
You will be notified whenever a record that you have chosen has been cited.
To manage your alert preferences, click on the button below.
Manage my Alerts
New Citation Alert!
Please log in to your account
- View Options
- References
- Media
- Tables
- Share
Abstract
The incomplete multi-view clustering (IMVC) aims to explore the consensus and complementary information embedded in incomplete multi-view data, so as to accurately aggregate all samples into different clusters. Existing IMVC methods still have the following issues that need to be further solved: how to efficiently (1) alleviate the computational complexity; (2) capture the complementary information from views; (3) uncover the high-order correlations underlying incomplete multi-view data. To address these problems, a new method, called view-specific anchors coupled tensorial bipartite graph learning for incomplete multi-view clustering (VA-TBGIMC), is proposed in this paper. Specifically, view-specific anchors are learned from non-missing samples to represent the distribution of all samples including missing ones. With the help of high-quality anchors, the bipartite graph between all samples and anchors is constructed, which can preserve the complementary information. Meanwhile, these bipartite graphs are stacked into a tensor which is imposed with a low-rank constraint to adequately capture the inter-view and inter-sample correlations from incomplete multi-view data. Further, the augmented graph of bipartite graph is constrained by Laplacian rank to learn a consensus cluster indicator. A large number of experiments validate the superiority and efficiency of the proposed method in terms of clustering performance and time consumption.
Highlights
•
A novel method is proposed to handle large-scale incomplete multi-view clustering.
•
View-specific anchors are learned to represent the original incomplete view data.
•
Bipartite graph between all samples and anchors is constructed for each view.
•
Bipartite graphs are stacked into a tensor to capture the high-order correlations.
•
Augmented graph is constrained to learn a consensus cluster indicator.
References
[1]
Y. Sun, Z. Ren, P. Hu, D. Peng, X. Wang, Hierarchical consensus hashing for cross-modal retrieval, IEEE Trans. Multimed. (2023) 1–14,.
Digital Library
[2]
Y. Sun, X. Wang, D. Peng, Z. Ren, X. Shen, Hierarchical hashing learning for image set classification, IEEE Trans. Image Process. 32 (2023) 1732–1744,.
Digital Library
[3]
Y. Sun, D. Peng, Z. Ren, Discrete aggregation hashing for image set classification, Expert Syst. Appl. 237 (2024).
[4]
G. Chao, S. Sun, J. Bi, A survey on multiview clustering, IEEE Trans. Artif. Intell. 2 (2) (2021) 146–168.
[5]
Q. Wang, M. Chen, F. Nie, X. Li, Detecting coherent groups in crowd scenes by multiview clustering, IEEE Trans. Pattern Anal. Mach. Intell. 42 (1) (2018) 46–58.
Digital Library
[6]
X. Zhang, Y. Yang, T. Li, Y. Zhang, H. Wang, H. Fujita, CMC: a consensus multi-view clustering model for predicting Alzheimer's disease progression, Comput. Methods Programs Biomed. 199 (2021).
[7]
X. Ma, X. Yan, J. Liu, G. Zhong, Simultaneous multi-graph learning and clustering for multiview data, Inf. Sci. 593 (2022) 472–487.
[8]
S. Huang, Y. Liu, I.W. Tsang, Z. Xu, J. Lv, Multi-view subspace clustering by joint measuring of consistency and diversity, IEEE Trans. Knowl. Data Eng. (2022).
[9]
M.-S. Chen, C.-D. Wang, D. Huang, J.-H. Lai, P.S. Yu, Efficient orthogonal multi-view subspace clustering, in: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 127–135.
[10]
J. You, Y. Hou, Z. Ren, X. You, J. Dai, Y. Yao, Cluster center consistency guided sampling learning for multiple kernel clustering, Inf. Sci. 606 (2022) 410–422.
[11]
J. Xu, Y. Ren, H. Tang, Z. Yang, L. Pan, Y. Yang, X. Pu, S.Y. Philip, L. He, Self-supervised discriminative feature learning for deep multi-view clustering, IEEE Trans. Knowl. Data Eng. (2022).
[12]
J. Wen, Z. Zhang, L. Fei, B. Zhang, Y. Xu, Z. Zhang, J. Li, A survey on incomplete multiview clustering, IEEE Trans. Syst. Man Cybern. Syst. 53 (2) (2022) 1136–1149.
[13]
M. Hu, S. Chen, Doubly aligned incomplete multi-view clustering, in: Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018, pp. 2262–2268.
[14]
J. Yin, S. Sun, Incomplete multi-view clustering with cosine similarity, Pattern Recognit. 123 (2022).
Digital Library
[15]
W. Xia, Q. Gao, Q. Wang, X. Gao, Tensor completion-based incomplete multiview clustering, IEEE Trans. Cybern. 52 (12) (2022) 13635–13644.
[16]
G. Ji, G.-F. Lu, One-step incomplete multiview clustering with low-rank tensor graph learning, Inf. Sci. 615 (2022) 209–225.
[17]
M. Yang, Y. Li, P. Hu, J. Bai, J. Lv, X. Peng, Robust multi-view clustering with incomplete information, IEEE Trans. Pattern Anal. Mach. Intell. 45 (1) (2022) 1055–1069.
[18]
J. Xu, C. Li, Y. Ren, L. Peng, Y. Mo, X. Shi, X. Zhu, Deep incomplete multi-view clustering via mining cluster complementarity, in: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, 2022, pp. 8761–8769.
[19]
Z. Kang, Z. Lin, X. Zhu, W. Xu, Structured graph learning for scalable subspace clustering: from single view to multiview, IEEE Trans. Cybern. 52 (9) (2021) 8976–8986.
[20]
W. Xia, Q. Gao, Q. Wang, X. Gao, C. Ding, D. Tao, Tensorized bipartite graph learning for multi-view clustering, IEEE Trans. Pattern Anal. Mach. Intell. 45 (4) (2022) 5187–5202.
[21]
J. Guo, J. Ye, Anchors bring ease: an embarrassingly simple approach to partial multi-view clustering, in: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, 2019, pp. 118–125.
[22]
W. He, Z. Zhang, Y. Chen, J. Wen, Structured anchor-inferred graph learning for universal incomplete multi-view clustering, World Wide Web 26 (1) (2023) 375–399.
[23]
S. Wang, X. Liu, L. Liu, W. Tu, X. Zhu, J. Liu, S. Zhou, E. Zhu, Highly-efficient incomplete large-scale multi-view clustering with consensus bipartite graph, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 9776–9785.
[24]
M. Li, S. Wang, X. Liu, S. Liu, Parameter-free and scalable incomplete multiview clustering with prototype graph, IEEE Trans. Neural Netw. Learn. Syst. (2022).
[25]
S. Liu, X. Liu, S. Wang, X. Niu, E. Zhu, Fast incomplete multi-view clustering with view-independent anchors, IEEE Trans. Neural Netw. Learn. Syst. (2022).
[26]
X. Yu, H. Liu, Y. Lin, Y. Wu, C. Zhang, Auto-weighted sample-level fusion with anchors for incomplete multi-view clustering, Pattern Recognit. 130 (2022).
[27]
J. Yin, R. Cai, S. Sun, Anchor-based incomplete multi-view spectral clustering, Neurocomputing 514 (2022) 526–538.
Digital Library
[28]
Z. Lv, Q. Gao, X. Zhang, Q. Li, M. Yang, View-consistency learning for incomplete multiview clustering, IEEE Trans. Image Process. 31 (2022) 4790–4802.
[29]
Z. Li, C. Tang, X. Zheng, X. Liu, W. Zhang, E. Zhu, High-order correlation preserved incomplete multi-view subspace clustering, IEEE Trans. Image Process. 31 (2022) 2067–2080.
[30]
D. Yan, L. Huang, M.I. Jordan, Fast approximate spectral clustering, in: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, pp. 907–916.
[31]
M.E. Kilmer, K. Braman, N. Hao, R.C. Hoover, Third-order tensors as operators on matrices: a theoretical and computational framework with applications in imaging, SIAM J. Matrix Anal. Appl. 34 (1) (2013) 148–172.
Digital Library
[32]
X. Li, H. Zhang, R. Wang, F. Nie, Multiview clustering: a scalable and parameter-free bipartite graph fusion method, IEEE Trans. Pattern Anal. Mach. Intell. 44 (1) (2020) 330–344.
[33]
K. Fan, On a theorem of Weyl concerning eigenvalues of linear transformations I, Proc. Natl. Acad. Sci. 35 (11) (1949) 652–655.
[34]
S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein, et al., Distributed optimization and statistical learning via the alternating direction method of multipliers, Found. Trends Mach. Learn. 3 (1) (2011) 1–122.
Digital Library
[35]
F. Nie, X. Wang, C. Deng, H. Huang, Learning a structured optimal bipartite graph for co-clustering, Adv. Neural Inf. Process. Syst. 30 (2017).
[36]
E. Esser, Applications of Lagrangian-based alternating direction methods and connections to split Bregman, CAM Rep. 9 (2009) 31.
[37]
J. Eckstein, D.P. Bertsekas, On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators, Math. Program. 55 (1992) 293–318.
[38]
L. Fei-Fei, R. Fergus, P. Perona, Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories, Comput. Vis. Image Underst. 106 (1) (2007) 59–70.
Digital Library
[39]
B. Wu, Y. Zhang, B.-G. Hu, Q. Ji, Constrained clustering and its application to face clustering in videos, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 3507–3514.
[40]
S. Liu, S. Wang, P. Zhang, K. Xu, X. Liu, C. Zhang, F. Gao, Efficient one-pass multi-view subspace clustering with consensus anchors, in: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, 2022, pp. 7576–7584.
[41]
A. Ng, M. Jordan, Y. Weiss, On spectral clustering: analysis and an algorithm, Adv. Neural Inf. Process. Syst. 14 (2001) 849–856.
[42]
J. Wen, Z. Zhang, Z. Zhang, L. Fei, M. Wang, Generalized incomplete multiview clustering with flexible locality structure diffusion, IEEE Trans. Cybern. 51 (1) (2020) 101–114.
[43]
L. Van der Maaten, G. Hinton, Visualizing data using t-SNE, J. Mach. Learn. Res. 9 (11) (2008) 2579–2605.
Recommendations
- Cross-view Graph Matching Guided Anchor Alignment for Incomplete Multi-view Clustering
Abstract
Multi-view bipartite graph clustering methods select a few representative anchors and then establish a connection with original samples to generate the bipartite graphs for clustering, which maintains impressive performance while reducing time ...
Highlights
- We observe a significant Cross-view Anchor-level Misalignment (CAM) problem for multiview bipartite graph clustering.
- We transform this tricky CAM problem to an easier cross-view cluster-level anchor alignment problem.
- We propose a ...
Read More
- Multiple kernel-based anchor graph coupled low-rank tensor learning for incomplete multi-view clustering
Abstract
Incomplete Multi-View Clustering (IMVC) attempts to give an optimal clustering solution for incomplete multi-view data that suffer from missing instances in certain views. However, most existing IMVC methods still have various drawbacks in ...
Read More
- Incomplete Multi-View Clustering with Regularized Hierarchical Graph
MM '23: Proceedings of the 31st ACM International Conference on Multimedia
In this article, we propose a novel and effective incomplete multi-view clustering (IMVC) framework, referred to as incomplete multi-view clustering with regularized hierarchical graph (IMVC_RHG). Different from the existing graph learning-based IMVC ...
Read More
Comments
Information & Contributors
Information
Published In
Information Sciences: an International Journal Volume 664, Issue C
Apr 2024
476 pages
ISSN:0020-0255
Issue’s Table of Contents
Elsevier Inc.
Publisher
Elsevier Science Inc.
United States
Publication History
Published: 25 June 2024
Author Tags
- Incomplete multi-view clustering
- Bipartite graph learning
- High-order correlations
- Low-rank tensor constraint
Qualifiers
- Research-article
Contributors
Other Metrics
View Article Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
Total Citations
Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Other Metrics
View Author Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in
Full Access
Get this Publication
Media
Figures
Other
Tables
