{"ID":2872250,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.09527","arxiv_id":"2509.09527","title":"Generative Diffusion Contrastive Network for Multi-View Clustering","abstract":"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.","short_abstract":"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 mult...","url_abs":"https://arxiv.org/abs/2509.09527","url_pdf":"https://arxiv.org/pdf/2509.09527v2","authors":"[\"Jian Zhu\",\"Xin Zou\",\"Xi Wang\",\"Lei Liu\",\"Chang Tang\",\"Li-Rong Dai\"]","published":"2025-09-11T15:09:26Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false,"code_links":[{"ID":609955,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2872250,"paper_url":"https://arxiv.org/abs/2509.09527","paper_title":"Generative Diffusion Contrastive Network for Multi-View Clustering","repo_url":"https://github.com/HackerHyper/GDCN","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}