Generative Diffusion Contrastive Network for Multi-View Clustering
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915737360138240 |
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| author | Zhu, Jian Zou, Xin Wang, Xi Liu, Lei Tang, Chang Dai, Li-Rong |
| author_facet | Zhu, Jian Zou, Xin Wang, Xi Liu, Lei Tang, Chang Dai, Li-Rong |
| contents | In recent years, Multi-View Clustering (MVC) has been significantly advanced under the influence of deep learning. By integrating heterogeneous data from multiple views, MVC enhances clustering analysis, making multi-view fusion critical to clustering performance. However, there is a problem of low-quality data in multi-view fusion. This problem primarily arises from two reasons: 1) Certain views are contaminated by noisy data. 2) Some views suffer from missing data. This paper proposes a novel Stochastic Generative Diffusion Fusion (SGDF) method to address this problem. SGDF leverages a multiple generative mechanism for the multi-view feature of each sample. It is robust to low-quality data. Building on SGDF, we further present the Generative Diffusion Contrastive Network (GDCN). Extensive experiments show that GDCN achieves the state-of-the-art results in deep MVC tasks. The source code is publicly available at https://github.com/HackerHyper/GDCN. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09527 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Diffusion Contrastive Network for Multi-View Clustering Zhu, Jian Zou, Xin Wang, Xi Liu, Lei Tang, Chang Dai, Li-Rong Computer Vision and Pattern Recognition In recent years, Multi-View Clustering (MVC) has been significantly advanced under the influence of deep learning. By integrating heterogeneous data from multiple views, MVC enhances clustering analysis, making multi-view fusion critical to clustering performance. However, there is a problem of low-quality data in multi-view fusion. This problem primarily arises from two reasons: 1) Certain views are contaminated by noisy data. 2) Some views suffer from missing data. This paper proposes a novel Stochastic Generative Diffusion Fusion (SGDF) method to address this problem. SGDF leverages a multiple generative mechanism for the multi-view feature of each sample. It is robust to low-quality data. Building on SGDF, we further present the Generative Diffusion Contrastive Network (GDCN). Extensive experiments show that GDCN achieves the state-of-the-art results in deep MVC tasks. The source code is publicly available at https://github.com/HackerHyper/GDCN. |
| title | Generative Diffusion Contrastive Network for Multi-View Clustering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.09527 |