skip to main content
10.1145/3664647.3681397acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

Robust Prototype Completion for Incomplete Multi-view Clustering

Published: 28 October 2024 Publication History

Abstract

In practical data collection processes, certain views may become partially unavailable due to sensor failures or equipment issues, leading to the problem of incomplete multi-view clustering (IMVC). While some IMVC methods employing prototype completion achieve satisfactory performance, almost all of them implicitly assume correct alignment of prototypes across all views. However, during prototype generation, different networks could generate different cluster centers, thereby leading to the produced prototypes from different views may be misaligned, \ie prototype noisy correspondence. To address this issue, we propose Robust Prototype Completion for Incomplete Multi-view Clustering (RPCIC), which mitigates the impact of noisy correspondence in prototypes. Specifically, RPCIC initially utilizes cross-view contrastive learning module to obtain consistent feature representations across different views. Subsequently, we devise robust contrastive loss for the produced prototypes, aiming to alleviate the influence of noisy correspondence within them. Finally, we employ prototype fusion-based strategy to complete the missing data. Comprehensive experiments demonstrate that RPCIC outperforms 11 state-of-the-art methods in terms of both performance and robustness. The code is available at https://github.com/hl-yuan/RPCIC.

References

[1]
Yongyong Chen, Xiaolin Xiao, and Yicong Zhou. 2019. Jointly learning kernel representation tensor and affinity matrix for multi-view clustering. IEEE Transactions on Multimedia, Vol. 22, 8 (2019), 1985--1997.
[2]
Li Fei-Fei, Rob Fergus, and Pietro Perona. 2004. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop. IEEE, 178--178.
[3]
Chuanxing Geng, Aiyang Han, and Songcan Chen. 2022. View-labels Are Indispensable: A Multifacet Complementarity Study of Multi-view Clustering. arXiv preprint arXiv:2205.02507 (2022).
[4]
Jan-Mark Geusebroek, Gertjan J Burghouts, and Arnold WM Smeulders. 2005. The Amsterdam library of object images. International Journal of Computer Vision, Vol. 61 (2005), 103--112.
[5]
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 16000--16009.
[6]
Zhenyu Huang, Guocheng Niu, Xiao Liu, Wenbiao Ding, Xinyan Xiao, Hua Wu, and Xi Peng. 2021. Learning with noisy correspondence for cross-modal matching. Advances in Neural Information Processing Systems, Vol. 34 (2021), 29406--29419.
[7]
Jiaqi Jin, Siwei Wang, Zhibin Dong, Xinwang Liu, and En Zhu. 2023. Deep incomplete multi-view clustering with cross-view partial sample and prototype alignment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 11600--11609.
[8]
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. 1989. Backpropagation applied to handwritten zip code recognition. Neural computation, Vol. 1, 4 (1989), 541--551.
[9]
Haobin Li, Yunfan Li, Mouxing Yang, Peng Hu, Dezhong Peng, and Xi Peng. 2023. Incomplete multi-view clustering via prototype-based imputation. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. 3911--3919.
[10]
Xingfeng Li, Yuangang Pan Pan, Yinghui Sun, Quansen Sun Sun, Ivor W. Tsang, and Zhenwen Ren. 2024. Fast Unpaired Multi-view Clustering. Proceedings of the 33rd International Joint Conference on Artificial Intelligence.
[11]
Xingfeng Li, Yinghui Sun, Quansen Sun, Zhenwen Ren, and Yuan Sun. 2023. Cross-view graph matching guided anchor alignment for incomplete multi-view clustering. Information Fusion, Vol. 100 (2023), 101941.
[12]
Yijie Lin, Yuanbiao Gou, Xiaotian Liu, Jinfeng Bai, Jiancheng Lv, and Xi Peng. 2022. Dual contrastive prediction for incomplete multi-view representation learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, 4 (2022), 4447--4461.
[13]
Yijie Lin, Yuanbiao Gou, Xiaotian Liu, Jinfeng Bai, Jiancheng Lv, and Xi Peng. 2023. Dual Contrastive Prediction for Incomplete Multi-View Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, 4 (2023), 4447--4461.
[14]
Yijie Lin, Yuanbiao Gou, Zitao Liu, Boyun Li, Jiancheng Lv, and Xi Peng. 2021. COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 11169--11178.
[15]
Yijie Lin, Yuanbiao Gou, Zitao Liu, Boyun Li, Jiancheng Lv, and Xi Peng. 2021. Completer: Incomplete multi-view clustering via contrastive prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 11174--11183.
[16]
Chengliang Liu, Jie Wen, Zhihao Wu, Xiaoling Luo, Chao Huang, and Yong Xu. 2023. Information recovery-driven deep incomplete multiview clustering network. IEEE Transactions on Neural Networks and Learning Systems (2023).
[17]
Chengliang Liu, Zhihao Wu, Jie Wen, Yong Xu, and Chao Huang. 2023. Localized Sparse Incomplete Multi-View Clustering. IEEE Transactions on Multimedia, Vol. 25 (2023), 5539--5551.
[18]
Xinwang Liu, Miaomiao Li, Chang Tang, Jingyuan Xia, Jian Xiong, Li Liu, Marius Kloft, and En Zhu. 2020. Efficient and effective regularized incomplete multi-view clustering. IEEE transactions on pattern analysis and machine intelligence, Vol. 43, 8 (2020), 2634--2646.
[19]
Xinwang Liu, Xinzhong Zhu, Miaomiao Li, Chang Tang, En Zhu, Jianping Yin, and Wen Gao. 2019. Efficient and effective incomplete multi-view clustering. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 4392--4399.
[20]
Yang Qin, Yuan Sun, Dezhong Peng, Joey Tianyi Zhou, Xi Peng, and Peng Hu. 2023. Cross-modal active complementary learning with self-refining correspondence. In Proceedings of the 37th International Conference on Neural Information Processing Systems. 24829--24840.
[21]
Yuan Sun, Yang Qin, Yongxiang Li, Dezhong Peng, Xi Peng, and Peng Hu. 2024. Robust Multi-View Clustering with Noisy Correspondence. IEEE Transactions on Knowledge and Data Engineering (2024), 1--14.
[22]
Yuan Sun, Yang Qin, Dezhong Peng, Zhenwen Ren, Chao Yang, and Peng Hu. 2024. Dual Self-Paced Hashing for Image Retrieval. IEEE Transactions on Multimedia (2024), 1--15.
[23]
Huayi Tang and Yong Liu. 2022. Deep safe incomplete multi-view clustering: Theorem and algorithm. In International Conference on Machine Learning. PMLR, 21090--21110.
[24]
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2019. Representation Learning with Contrastive Predictive Coding. arxiv: 1807.03748 [cs.LG]
[25]
Jiatai Wang, Zhiwei Xu, Xuewen Yang, Dongjin Guo, and Limin Liu. 2023. Self-supervised image clustering from multiple incomplete views via constrastive complementary generation. IET Computer Vision, Vol. 17, 2 (2023), 189--202.
[26]
Qianqian Wang, Zhengming Ding, Zhiqiang Tao, Quanxue Gao, and Yun Fu. 2021. Generative partial multi-view clustering with adaptive fusion and cycle consistency. IEEE Transactions on Image Processing, Vol. 30 (2021), 1771--1783.
[27]
Jie Wen, Chengliang Liu, Shijie Deng, Yicheng Liu, Lunke Fei, Ke Yan, and Yong Xu. 2023. Deep double incomplete multi-view multi-label learning with incomplete labels and missing views. IEEE transactions on neural networks and learning systems (2023).
[28]
Jie Wen, Gehui Xu, Zhanyan Tang, Wei Wang, Lunke Fei, and Yong Xu. 2023. Graph Regularized and Feature Aware Matrix Factorization for Robust Incomplete Multi-view Clustering. IEEE Transactions on Circuits and Systems for Video Technology (2023), 3728 -- 3741.
[29]
Jie Wen, Yong Xu, and Hong Liu. 2020. Incomplete Multiview Spectral Clustering With Adaptive Graph Learning. IEEE Transactions on Cybernetics, Vol. 50, 4 (2020), 1418--1429.
[30]
Jie Wen, Ke Yan, Zheng Zhang, Yong Xu, Junqian Wang, Lunke Fei, and Bob Zhang. 2020. Adaptive graph completion based incomplete multi-view clustering. IEEE Transactions on Multimedia, Vol. 23 (2020), 2493--2504.
[31]
J Wen, Z Zhang, L Fei, and M Wang. 2020. Generalized Incomplete Multiview Clustering With Flexible Locality Structure Diffusion. IEEE Transactions on Cybernetics, Vol. 51, 1 (2020), 101--114.
[32]
Jie Wen, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu, Zhao Zhang, and Jinxing Li. 2022. A survey on incomplete multiview clustering. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 53, 2 (2022), 1136--1149.
[33]
Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang, Lunke Fei, and Hong Liu. 2019. Unified embedding alignment with missing views inferring for incomplete multi-view clustering. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 5393--5400.
[34]
Lior Wolf, Tal Hassner, and Itay Maoz. 2011. Face recognition in unconstrained videos with matched background similarity. In CVPR 2011. IEEE, 529--534.
[35]
Chang Xu, Dacheng Tao, and Chao Xu. 2013. A survey on multi-view learning. arXiv preprint arXiv:1304.5634 (2013).
[36]
Jie Xu, Chao Li, Liang Peng, Yazhou Ren, Xiaoshuang Shi, Heng Tao Shen, and Xiaofeng Zhu. 2023. Adaptive feature projection with distribution alignment for deep incomplete multi-view clustering. IEEE Transactions on Image Processing, Vol. 32 (2023), 1354--1366.
[37]
Jie Xu, Chao Li, Liang Peng, Yazhou Ren, Xiaoshuang Shi, Heng Tao Shen, and Xiaofeng Zhu. 2023. Adaptive Feature Projection With Distribution Alignment for Deep Incomplete Multi-View Clustering. IEEE Transactions on Image Processing, Vol. 32 (2023), 1354--1366.
[38]
Jie Xu, Chao Li, Yazhou Ren, Liang Peng, Yujie Mo, Xiaoshuang Shi, and Xiaofeng Zhu. 2022. Deep Incomplete Multi-View Clustering via Mining Cluster Complementarity. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). 8761--8769.
[39]
Mouxing Yang, Yunfan Li, Peng Hu, Jinfeng Bai, Jiancheng Lv, and Xi Peng. 2022. Robust multi-view clustering with incomplete information. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, 1 (2022), 1055--1069.
[40]
Mouxing Yang, Yunfan Li, Peng Hu, Jinfeng Bai, Jiancheng Lv, and Xi Peng. 2023. Robust Multi-View Clustering With Incomplete Information. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, 1 (2023), 1055--1069.
[41]
Xihong Yang, Jin Jiaqi, Siwei Wang, Ke Liang, Yue Liu, Yi Wen, Suyuan Liu, Sihang Zhou, Xinwang Liu, and En Zhu. 2023. Dealmvc: Dual contrastive calibration for multi-view clustering. In Proceedings of the 31st ACM International Conference on Multimedia. 337--346.
[42]
Jiali You, Zhenwen Ren, Quansen Sun, Yuan Sun, and Xingfeng Li. 2022. Approximate shifted laplacian reconstruction for multiple kernel clustering. In Proceedings of the 30th ACM International Conference on Multimedia. 2862--2870.
[43]
Chao Zhang, Xiuyi Jia, Zechao Li, Chunlin Chen, and Huaxiong Li. 2024. Learning Cluster-Wise Anchors for Multi-View Clustering. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 16696--16704.
[44]
Chao Zhang, Huaxiong Li, Wei Lv, Zizheng Huang, Yang Gao, and Chunlin Chen. 2023. Enhanced tensor low-rank and sparse representation recovery for incomplete multi-view clustering. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 11174--11182.
[45]
Kaiwu Zhang, Shiqiang Du, Baokai Liu, and Shengxia Gao. 2023. Preventing Dimensional Collapse of Incomplete Multi-View Clustering via Direct Contrastive Learning. arXiv preprint arXiv:2303.12241 (2023).
[46]
Qian Zhang, Zhao Kang, Zenglin Xu, Shudong Huang, and Hongguang Fu. 2022. Spaks: Self-paced multiple kernel subspace clustering with feature smoothing regularization. Knowledge-Based Systems, Vol. 253 (2022), 109500.
[47]
Zhilu Zhang and Mert R Sabuncu. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. (2018), 8792--8802.
[48]
Handong Zhao, Hongfu Liu, and Yun Fu. 2016. Incomplete multi-modal visual data grouping. In IJCAI. 2392--2398.

Cited By

View all
  • (2025)Fast multi-view clustering via anchor label transmit with tensor structure constraintExpert Systems with Applications10.1016/j.eswa.2025.126878274(126878)Online publication date: May-2025

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia
October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. incomplete multi-view clustering
  2. prototype completion
  3. prototype noisy correspondence.

Qualifiers

  • Research-article

Funding Sources

  • Base Strengthening Program of National Defense Science and Technology

Conference

MM '24
Sponsor:
MM '24: The 32nd ACM International Conference on Multimedia
October 28 - November 1, 2024
Melbourne VIC, Australia

Acceptance Rates

MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)137
  • Downloads (Last 6 weeks)71
Reflects downloads up to 01 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Fast multi-view clustering via anchor label transmit with tensor structure constraintExpert Systems with Applications10.1016/j.eswa.2025.126878274(126878)Online publication date: May-2025

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media