This paper has been published in Peerj Computer Science.
Advances in deep learning have propelled the evolution of multi-view clustering techniques, which strive to obtain a view-common representation from multi-view datasets. However, the contemporary multi-view clustering community confronts two prominent challenges. One is that view-specific representations lack guarantees to reduce noise introduction, and another is that the fusion process compromises view-specific representations, resulting in the inability to capture efficient information from multi-view data. This may negatively affect the accuracy of the clustering results. In this paper, we introduce a novel technique named the "contrastive attentive strategy" to address the above problems. Our approach effectively extracts robust view-specific representations from multi-view data with reduced noise while preserving view completeness. This results in the extraction of consistent representations from multi-view data while preserving the features of view-specific representations. We integrate view-specific encoders, a hybrid attentive module, a fusion module, and deep clustering into a unified framework called AMCFCN. Experimental results on four multi-view datasets demonstrate that our method, AMCFCN, outperforms seven competitive multi-view clustering methods.
- Python 3.9.7
- PyTorch 1.8.0
- CUDA 11.4
All our experiments are put in ./experiments, data files under data/processed.
Note: Before you run the program firstly, you should run datatool/load_dataset to generate dataset.
You could quickly run our experiments by: python train.py -c [config name].
For example: python train.py -c emnist
Note: the datatool.py can automatically process the raw data like E-MNIST, FashionMNIST, COIL-20, and COIL-100.