Generative Diffusion Contrastive Network for Multi-View Clustering

Authors: Jian Zhu, Xin Zou, Xi Wang, Lei Liu, Chang Tang, Li-Rong Dai.

This paper is submitted to the International Conference on Acoustics, Speech, and Signal Processing (ICASSP2026). Generative Diffusion Contrastive Network for Multi-View Clustering

1. 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.

2.Requirements

pytorch==1.12.1

numpy>=1.21.6

scikit-learn>=1.0.2

3.Datasets

The Synthetic3d, Prokaryotic, and MNIST-USPS datasets are placed in "data" folder. The others dataset could be downloaded from cloud. key: data

4.Usage

• an example for train a new model：

python train.py

• You can get the following output:

Epoch 285 Loss:14.258701
Epoch 286 Loss:14.277878
Epoch 287 Loss:14.265236
Epoch 288 Loss:14.249377
Epoch 289 Loss:14.250543
Epoch 290 Loss:14.231292
Epoch 291 Loss:14.204707
Epoch 292 Loss:14.229280
Epoch 293 Loss:14.224252
Epoch 294 Loss:14.226442
Epoch 295 Loss:14.225920
Epoch 296 Loss:14.204377
Epoch 297 Loss:14.204086
Epoch 298 Loss:14.188799
Epoch 299 Loss:14.208624
Epoch 300 Loss:14.205029
---------train over---------
Clustering results:
ACC = 0.9800 NMI = 0.9357 PUR=0.9800 ARI = 0.9506

5.Acknowledgments

Work&Code is inspired by MFLVC, CONAN, CoMVC ...

If you have any problems, contact me via qijian.zhu@outlook.com.