Cited By
View all- Zhao LXie Q(2024)Distribution-Level Multi-View Clustering for Unaligned DataIEEE Signal Processing Letters10.1109/LSP.2024.344094831(2330-2334)Online publication date: 2024
MVCIR-net: Multi-view Clustering Information Reinforcement Network
Pages 3609 - 3618
Abstract
Multi-view clustering (MVC) integrates information from different views to improve clustering performance compared to single-view clustering. However, the raw multi-view data in the feature space often contains irrelevant information to the clustering task, which is difficult to separate using existing methods. This irrelevant information is processed equally with clustering information, negatively impacting the final clustering performance. In this paper, we propose a new framework for multi-view clustering information reinforcement network (MVCIR-net) to alleviate these problems. Our method gives practical clustering meaning to the clustering distribution layer by contrastive learning. Then, the trusted neighbor instances distribution of the normalized graph is debias aggregated to form the clustering information propensity distribution, and the clustering information distribution is made to fit this distribution. In addition, the coupling degree of the clustering information distribution in different views on the same sample should be enhanced. Through the aforementioned strategies, the raw data is fuzzy mapped into clustering information, and the network's ability to recognize clustering information is strengthened. Finally, the fuzzy mapping data is input into the network and reconstructed to evaluate the quality of the extracted clustering information. Extensive experiments on public multi-view datasets show that MVCIR-net achieves superior clustering effectiveness and the ability to identify clustering information.
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Index Terms
- MVCIR-net: Multi-view Clustering Information Reinforcement Network
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Published In
October 2023
9913 pages
ISBN:9798400701085
DOI:10.1145/3581783
- General Chairs:
- Abdulmotaleb El Saddik,
- Tao Mei,
- Rita Cucchiara,
- Program Chairs:
- Marco Bertini,
- Diana Patricia Tobon Vallejo,
- Pradeep K. Atrey,
- M. Shamim Hossain
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Publication History
Published: 27 October 2023
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- the High-level Talent Program
- the Science and Technology Project of Liaoning Province
- the Youth Science and Technology Star Support Program of Dalian City
- the Fundamental Research Funds for the Central Universities
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MM '23
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MM '23: The 31st ACM International Conference on Multimedia
October 29 - November 3, 2023
Ottawa ON, Canada
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Cited By
View all- Zhao LXie Q(2024)Distribution-Level Multi-View Clustering for Unaligned DataIEEE Signal Processing Letters10.1109/LSP.2024.344094831(2330-2334)Online publication date: 2024
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