Abstract
The assumption of data completeness plays a significant role in the effectiveness of current Multi-view Clustering (MVC) methods. However, data collection and transmission would unavoidably breach this assumption, resulting in the Partially Data-missing Problem (PDP). A common remedy is to first impute missing values and then conduct MVC methods, which may cause performance degeneration due to inaccurate imputation. To address these issues in PDP, we introduce an imputation-free framework that utilizes a matrix correction technique, employing a novel two-stage strategy termed ’correction-clustering’. In the first stage, we correct distance matrices derived from incomplete data and compute affinity matrices. Following this, we integrate them with affinity-based MVC methods. This approach circumvents the uncertainties associated with inaccurate imputations, enhancing clustering performance. Comprehensive experiments show that our method outperforms traditional imputation-based techniques, yielding superior clustering results across various levels of missing data.
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Acknowledgements
We appreciate the anonymous reviewers for their helpful feedback that greatly improved this paper. The work of Fangchen Yu and Wenye Li was supported in part by Guangdong Basic and Applied Basic Research Foundation (2021A1515011825), Guangdong Introducing Innovative and Entrepreneurial Teams Fund (2017ZT07X152), Shenzhen Science and Technology Program (CUHKSZWDZC0004), and Shenzhen Research Institute of Big Data Scholarship Program. The work of Yuqi Ma was supported in part by CUHKSZ-SRIBD Joint PhD Program. The work of Jianfeng Mao was supported in part by National Natural Science Foundation of China under grant U1733102, in part by the Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen under grant B10120210117, and in part by CUHK-Shenzhen under grant PF.01.000404.
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Yu, F., Shi, Z., Ma, Y., Mao, J., Li, W. (2024). From Incompleteness to Unity: A Framework for Multi-view Clustering with Missing Values. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_9
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