Reliable Attribute-missing Multi-view Clustering with Instance-level and feature-level Cooperative Imputation
Authors: 
Dayu Hu, Suyuan Liu, Jun Wang, Junpu Zhang, Siwei Wang, Xingchen Hu, Xinzhong Zhu, Chang Tang, Xinwang LiuAuthors Info & Claims
Dayu Hu, Suyuan Liu, Jun Wang, Junpu Zhang, Siwei Wang, Xingchen Hu, Xinzhong Zhu, Chang Tang, Xinwang LiuAuthors Info & ClaimsPages 1456 - 1466
Abstract
Multi-view clustering (MVC) constitutes a distinct approach to data mining within the field of machine learning. Due to limitations in the data collection process, missing attributes are frequently encountered. However, existing MVC methods primarily focus on missing instances, showing limited attention to missing attributes. A small number of studies employ the reconstruction of missing instances to address missing attributes, potentially overlooking the synergistic effects between the instance and feature spaces, which could lead to distorted imputation outcomes. Furthermore, current methods uniformly treat all missing attributes as zero values, thus failing to differentiate between real and technical zeroes, potentially resulting in data over-imputation. To mitigate these challenges, we introduce a novel Reliable Attribute-Missing Multi-View Clustering method (RAM-MVC). Specifically, feature reconstruction is utilized to address missing attributes, while similarity graphs are simultaneously constructed within the instance and feature spaces. By leveraging structural information from both spaces, RAM-MVC learns a high-quality feature reconstruction matrix during the joint optimization process. Additionally, we introduce a reliable imputation guidance module that distinguishes between real and technical attribute-missing events, enabling discriminative imputation. The proposed RAM-MVC method outperforms nine baseline methods, as evidenced by real-world experiments using single-cell multi-view data.
References
[1]
Brian B Avants, Nicholas J Tustison, and James R Stone. 2021. Similarity-driven multi-view embeddings from high-dimensional biomedical data. Nature computational science, Vol. 1, 2 (2021), 143--152.
[2]
Md Bahadur Badsha, Rui Li, Boxiang Liu, Yang I Li, Min Xian, Nicholas E Banovich, and Audrey Qiuyan Fu. 2020. Imputation of single-cell gene expression with an autoencoder neural network. Quantitative Biology, Vol. 8, 1 (2020), 78--94.
[3]
Tanya Barrett, Tugba O Suzek, Dennis B Troup, Stephen E Wilhite, Wing-Chi Ngau, Pierre Ledoux, Dmitry Rudnev, Alex E Lash, Wataru Fujibuchi, and Ron Edgar. 2005. NCBI GEO: mining millions of expression profiles-database and tools. Nucleic acids research, Vol. 33, suppl_1 (2005), D562--D566.
[4]
Xiao Cai, Feiping Nie, and Heng Huang. 2013. Multi-view k-means clustering on big data. In Twenty-Third International Joint conference on artificial intelligence.
[5]
Zhiwen Cao and Xijiong Xie. 2024. Multi-view unsupervised complementary feature selection with multi-order similarity learning. Knowledge-Based Systems, Vol. 283 (2024), 111172.
[6]
Guoqing Chao, Yi Jiang, and Dianhui Chu. 2024. Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 11221--11229.
[7]
Zhe Chen, Xiao-Jun Wu, Tianyang Xu, and Josef Kittler. 2023. Fast self-guided multi-view subspace clustering. IEEE transactions on image processing (2023).
[8]
Wanqiu Cui, Junping Du, Dawei Wang, Feifei Kou, and Zhe Xue. 2021. MVGAN: Multi-view graph attention network for social event detection. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 12, 3 (2021), 1--24.
[9]
Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst, and Nikolai Nefedov. 2012. Clustering with multi-layer graphs: A spectral perspective. IEEE Transactions on Signal Processing, Vol. 60, 11 (2012), 5820--5831.
[10]
Haitao Fu, Feng Huang, Xuan Liu, Yang Qiu, and Wen Zhang. 2022. MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks. Bioinformatics, Vol. 38, 2 (2022), 426--434.
[11]
Ellen Goeleven, Rudi De Raedt, Lemke Leyman, and Bruno Verschuere. 2008. The Karolinska directed emotional faces: a validation study. Cognition and emotion, Vol. 22, 6 (2008), 1094--1118.
[12]
Renxiang Guan, Zihao Li, Wenxuan Tu, Jun Wang, Yue Liu, Xianju Li, Chang Tang, and Ruyi Feng. 2024. Contrastive Multiview Subspace Clustering of Hyperspectral Images Based on Graph Convolutional Networks. IEEE Transactions on Geoscience and Remote Sensing, Vol. 62 (2024), 1--14.
[13]
Wenyu Hao, Shanmin Pang, Xiuxiu Bai, and Jianru Xue. 2023. Tensor-Based Incomplete Multi-View Clustering With Low-Rank Data Reconstruction and Consistency Guidance. IEEE Transactions on Circuits and Systems for Video Technology (2023).
[14]
Yaru Hao, Xiao-Yuan Jing, Runhang Chen, and Wei Liu. 2023. Learning enhanced specific representations for multi-view feature learning. Knowledge-Based Systems, Vol. 272 (2023), 110590.
[15]
Dayu Hu, Zhibin Dong, Ke Liang, Hao Yu, Siwei Wang, and Xinwang Liu. 2024. High-order Topology for Deep Single-cell Multi-view Fuzzy Clustering. IEEE Transactions on Fuzzy Systems (2024).
[16]
Dayu Hu, Ke Liang, Zhibin Dong, Jun Wang, Yawei Zhao, and Kunlun He. 2024. Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data. Briefings in Bioinformatics, Vol. 25, 2 (2024), bbae102.
[17]
Dong Huang, Chang-Dong Wang, and Jian-Huang Lai. 2023. Fast multi-view clustering via ensembles: Towards scalability, superiority, and simplicity. IEEE Transactions on Knowledge and Data Engineering (2023).
[18]
Zhao Kang, Zhiping Lin, Xiaofeng Zhu, and Wenbo Xu. 2021. Structured graph learning for scalable subspace clustering: From single view to multiview. IEEE Transactions on Cybernetics, Vol. 52, 9 (2021), 8976--8986.
[19]
Junlong Ke, Zichen Wen, Yechenhao Yang, Chenhang Cui, Yazhou Ren, Xiaorong Pu, and Lifang He. 2024. Integrating Vision-Language Semantic Graphs in Multi-View Clustering. In IJCAI.
[20]
Youngjune Lee, Yeongjong Jeong, Keunchan Park, and SeongKu Kang. 2023. MvFS: Multi-view Feature Selection for Recommender System. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4048--4052.
[21]
Feng Li, Yang Liu, Jinxing Liu, Daohui Ge, and Junliang Shang. 2024. A framework for scRNA-seq data clustering based on multi-view feature integration. Biomedical Signal Processing and Control, Vol. 89 (2024), 105785.
[22]
Lusi Li, Zhiqiang Wan, and Haibo He. 2021. Incomplete multi-view clustering with joint partition and graph learning. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 1 (2021), 589--602.
[23]
Wei Vivian Li and Jingyi Jessica Li. 2018. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nature communications, Vol. 9, 1 (2018), 997.
[24]
Xingfeng Li, Yinghui Sun, Quansen Sun, Jia Dai, and Zhenwen Ren. 2023. Distribution Consistency based Fast Anchor Imputation for Incomplete Multi-view Clustering. In Proceedings of the 31st ACM International Conference on Multimedia. 368--376.
[25]
Xiang-Long Li, Man-Sheng Chen, Chang-Dong Wang, and Jian-Huang Lai. 2022. Refining graph structure for incomplete multi-view clustering. IEEE transactions on neural networks and learning systems (2022).
[26]
Yifeng Li, Fang-Xiang Wu, and Alioune Ngom. 2018. A review on machine learning principles for multi-view biological data integration. Briefings in bioinformatics, Vol. 19, 2 (2018), 325--340.
[27]
Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun, and Kunlun He. 2024. A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).
[28]
Ke Liang, Lingyuan Meng, Sihang Zhou, Wenxuan Tu, Siwei Wang, Yue Liu, Meng Liu, Long Zhao, Xiangjun Dong, and Xinwang Liu. 2024. MINES: Message Intercommunication for Inductive Relation Reasoning over Neighbor-Enhanced Subgraphs. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 10645--10653.
[29]
Naiyao Liang, Zuyuan Yang, and Shengli Xie. 2022. Incomplete multi-view clustering with sample-level auto-weighted graph fusion. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 6 (2022), 6504--6511.
[30]
Xiang Lin, Tian Tian, Zhi Wei, and Hakon Hakonarson. 2022. Clustering of single-cell multi-omics data with a multimodal deep learning method. Nature communications, Vol. 13, 1 (2022), 7705.
[31]
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.
[32]
Zerun Lin and Le Ou-Yang. 2023. Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning. Briefings in Bioinformatics, Vol. 24, 1 (2023), bbac586.
[33]
George C Linderman, Jun Zhao, Manolis Roulis, Piotr Bielecki, Richard A Flavell, Boaz Nadler, and Yuval Kluger. 2022. Zero-preserving imputation of single-cell RNA-seq data. Nature communications, Vol. 13, 1 (2022), 192.
[34]
Cheng Liu, Si Wu, Rui Li, Dazhi Jiang, and Hau-San Wong. 2023. Self-supervised graph completion for incomplete multi-view clustering. IEEE Transactions on Knowledge and Data Engineering (2023).
[35]
Suyuan Liu, Junpu Zhang, Yi Wen, Xihong Yang, Siwei Wang, Yi Zhang, En Zhu, Chang Tang, Long Zhao, and Xinwang Liu. 2024. Sample-Level Cross-View Similarity Learning for Incomplete Multi-View Clustering. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 14017--14025.
[36]
Zihuan Liu, Tong Liu, Wenke Sun, Yongzhong Zhao, and Xiaodi Wang. 2022. M-band wavelet-based imputation of scRNA-seq matrix and multi-view clustering of cell. bioRxiv (2022), 2022--12.
[37]
Romain Lopez, Jeffrey Regier, Michael B Cole, Michael I Jordan, and Nir Yosef. 2018. Deep generative modeling for single-cell transcriptomics. Nature methods, Vol. 15, 12 (2018), 1053--1058.
[38]
Feiping Nie, Jing Li, Xuelong Li, et al. 2016. Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In IJCAI, Vol. 9. 1881--1887.
[39]
Feiping Nie, Zhenyu Ma, Jingyu Wang, and Xuelong Li. 2023. Fast sparse discriminative k-means for unsupervised feature selection. IEEE Transactions on Neural Networks and Learning Systems (2023).
[40]
Feiping Nie, Xiaoqian Wang, and Heng Huang. 2014. Clustering and projected clustering with adaptive neighbors. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 977--986.
[41]
Xin Peng, Jieren Cheng, Xiangyan Tang, Bin Zhang, and Wenxuan Tu. 2024. Multi-view graph imputation network. Information Fusion, Vol. 102 (2024), 102024.
[42]
Jingyu Pu, Chenhang Cui, Xinyue Chen, Yazhou Ren, Xiaorong Pu, Zhifeng Hao, S Yu Philip, and Lifang He. 2024. Adaptive Feature Imputation with Latent Graph for Deep Incomplete Multi-View Clustering. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 14633--14641.
[43]
Jingyu Pu, Chenhang Cui, Yazhou Ren, Zichen Wen, Tianyi Wu, Xiaorong Pu, and Lifang He. 2023. Deep Multi-View Clustering via View-Specific Representation and Global Graph. In 2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 493--502.
[44]
Hao Quan, Xinjia Li, Dayu Hu, Tianhang Nan, and Xiaoyu Cui. 2024. Dual-channel prototype network for few-shot pathology image classification. IEEE Journal of Biomedical and Health Informatics (2024).
[45]
Chang Tang, Zhenglai Li, Jun Wang, Xinwang Liu, Wei Zhang, and En Zhu. 2022. Unified one-step multi-view spectral clustering. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 6 (2022), 6449--6460.
[46]
Chang Tang, Xiao Zheng, Xinwang Liu, Wei Zhang, Jing Zhang, Jian Xiong, and Lizhe Wang. 2021. Cross-view locality preserved diversity and consensus learning for multi-view unsupervised feature selection. IEEE Transactions on Knowledge and Data Engineering, Vol. 34, 10 (2021), 4705--4716.
[47]
Huayi Tang and Yong Liu. 2022. Deep safe incomplete multi-view clustering: Theorem and algorithm. In International Conference on Machine Learning. PMLR, 21090--21110.
[48]
Zhiqiang Tao, Hongfu Liu, Huazhu Fu, and Yun Fu. 2019. Multi-view saliency-guided clustering for image cosegmentation. IEEE Transactions on Image Processing, Vol. 28, 9 (2019), 4634--4645.
[49]
Xinhang Wan, Bin Xiao, Xinwang Liu, Jiyuan Liu, Weixuan Liang, and En Zhu. 2024. Fast Continual Multi-View Clustering With Incomplete Views. IEEE Transactions on Image Processing, Vol. 33 (2024), 2995--3008. https://doi.org/10.1109/TIP.2024.3388974
[50]
Menghan Wang, Yujie Lin, Guli Lin, Keping Yang, and Xiao-ming Wu. 2020. M2GRL: A multi-task multi-view graph representation learning framework for web-scale recommender systems. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2349--2358.
[51]
Yiming Wang, Dongxia Chang, Zhiqiang Fu, Jie Wen, and Yao Zhao. 2022. Incomplete multiview clustering via cross-view relation transfer. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 33, 1 (2022), 367--378.
[52]
Jie Wen, Zheng Zhang, Zhao Zhang, Lei Zhu, Lunke Fei, Bob Zhang, and Yong Xu. 2021. Unified tensor framework for incomplete multi-view clustering and missing-view inferring. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 10273--10281.
[53]
Zichen Wen, Yawen Ling, Yazhou Ren, Tianyi Wu, Jianpeng Chen, Xiaorong Pu, Zhifeng Hao, and Lifang He. 2024. Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering. In AAAI. 15841--15849.
[54]
Yangyang Wu, Xiaoye Miao, Xinyu Huang, and Jianwei Yin. 2023. Jointly imputing multi-view data with optimal transport. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 4747--4755.
[55]
Shiting Xiao, Yufu Wang, Ammon Perkes, Bernd Pfrommer, Marc Schmidt, Kostas Daniilidis, and Marc Badger. 2023. Multi-view tracking, re-id, and social network analysis of a flock of visually similar birds in an outdoor aviary. International Journal of Computer Vision, Vol. 131, 6 (2023), 1532--1549.
[56]
Gehui Xu, Jie Wen, Chengliang Liu, Bing Hu, Yicheng Liu, Lunke Fei, and Wei Wang. 2024. Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 16147--16155.
[57]
Ben Yang, Xuetao Zhang, Feiping Nie, Fei Wang, Weizhong Yu, and Rong Wang. 2020. Fast multi-view clustering via nonnegative and orthogonal factorization. IEEE Transactions on Image Processing, Vol. 30 (2020), 2575--2586.
[58]
Liying Yang, Zhenwei Zhu, Xuxin Lin, Jian Nong, and Yanyan Liang. 2023 d. Long-Range Grouping Transformer for Multi-View 3D Reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 18257--18267.
[59]
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.
[60]
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.
[61]
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.
[62]
Xihong Yang, Yue Liu, Sihang Zhou, Siwei Wang, Wenxuan Tu, Qun Zheng, Xinwang Liu, Liming Fang, and En Zhu. 2023. Cluster-guided Contrastive Graph Clustering Network. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 10834--10842.
[63]
Fangchen Yu, Zhan Shi, Yuqi Ma, Jianfeng Mao, and Wenye Li. 2023. From Incompleteness to Unity: A Framework for Multi-view Clustering with Missing Values. In International Conference on Neural Information Processing. Springer, 101--112.
[64]
Haoliang Yuan, Junyu Li, Yong Liang, and Yuan Yan Tang. 2022. Multi-view unsupervised feature selection with tensor low-rank minimization. Neurocomputing, Vol. 487 (2022), 75--85.
[65]
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.
[66]
Rui Zhang, Yunxing Zhang, and Xuelong Li. 2020. Unsupervised feature selection via adaptive graph learning and constraint. IEEE Transactions on neural networks and learning systems, Vol. 33, 3 (2020), 1355--1362.
[67]
Wei Zhang, Zhaohong Deng, Te Zhang, Kup-Sze Choi, Jun Wang, and Shitong Wang. 2021. Incomplete multiple view fuzzy inference system with missing view imputation and cooperative learning. IEEE Transactions on Fuzzy Systems, Vol. 30, 8 (2021), 3038--3051.
[68]
Zhenwei Zhu, Liying Yang, Ning Li, Chaohao Jiang, and Yanyan Liang. 2023. Umiformer: Mining the correlations between similar tokens for multi-view 3d reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 18226--18235.
[69]
Ruohan Zong, Yang Zhang, Lanyu Shang, and Dong Wang. 2023. Contrastfaux: Sparse semi-supervised fauxtography detection on the web using multi-view contrastive learning. In Proceedings of the ACM Web Conference 2023. 3994--4003.
[70]
Chunman Zuo and Luonan Chen. 2021. Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data. Briefings in Bioinformatics, Vol. 22, 4 (2021), bbaa287.
[71]
Chunman Zuo, Hao Dai, and Luonan Chen. 2021. Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data. Bioinformatics, Vol. 37, 22 (2021), 4091--4099. new
Index Terms
- Reliable Attribute-missing Multi-view Clustering with Instance-level and feature-level Cooperative Imputation
Recommendations
Incomplete multi-view clustering with multiple imputation and ensemble clustering
AbstractMulti-view clustering is an important and challenging task in machine learning and data mining. In the past decade, this topic attracted much attention and there have been many progress achieved in this field. However, in reality, due to different ...
Double-level View-correlation Multi-view Subspace Clustering
AbstractIn recent years, significant progress has been made in Multi-view Subspace Clustering (MSC). Most existing MSC methods attempt to explore and exploit the view correlations of multi-view data to boost the clustering performance. They achieve the ...
Multi-view clustering via spectral partitioning and local refinement
A new multi-view clustering algorithm is proposed.The proposed MVNC algorithm uses spectral partitioning and local refinement.MVNC is compared to state-of-the-art algorithms using three real-world datasets.MVNC significantly outperforms the other ...
Comments
Information & Contributors
Information
Published In
October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
- General Chairs:
- Jianfei Cai,
- Mohan Kankanhalli,
- Balakrishnan Prabhakaran,
- Susanne Boll,
- Program Chairs:
- Ramanathan Subramanian,
- Liang Zheng,
- Vivek K. Singh,
- Pablo Cesar,
- Lexing Xie,
- Dong Xu
Copyright © 2024 ACM.
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
Check for updates
Author Tags
Qualifiers
- Research-article
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
- 0Total Citations
- 313Total Downloads
- Downloads (Last 12 months)313
- Downloads (Last 6 weeks)218
Reflects downloads up to 08 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in