Generative Diffusion Contrastive Network for Multi-View Clustering
This work addresses data quality issues in multi-view clustering, which is important for applications involving heterogeneous data, but it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of low-quality data in multi-view clustering, such as noise and missing data, by proposing a Generative Diffusion Contrastive Network (GDCN) that achieves state-of-the-art results in deep multi-view clustering tasks.
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.