用生成扩散模型融合多视图数据，提升噪声和缺失数据下的聚类效果。
Generative Diffusion Contrastive Network for Multi-View Clustering
- 通过生成式扩散机制融合多视图特征，增强对低质数据的鲁棒性。
- 在多个基准数据集上达到当前最优聚类性能，显著优于已有方法。
- 适合处理存在噪声或缺失数据的复杂多视图聚类任务。
近年来，深度学习推动了多视图聚类（MVC）的快速发展。通过融合来自多个视图的异构数据，MVC提升了聚类分析能力，但多视图融合常面临低质量数据问题：部分视图受噪声污染，另一些视图存在缺失数据。本文提出一种新型随机生成扩散融合（SGDF）方法，利用多生成机制处理每个样本的多视图特征，具备强鲁棒性。在此基础上，进一步构建生成扩散对比网络（GDCN）。大量实验表明，GDCN在深度多视图聚类任务中达到当前最优表现。源代码已公开于 https://github.com/HackerHyper/GDCN。
原文摘要 · Abstract (English)
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.
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