Abstract: With the development of big data, deep learning has made remarkable progress on multi-view clustering. Multi-view fusion is a crucial technique for the model obtaining a common representation. However, existing literature adopts shallow fusion strategies, such as weighted-sum fusion and concatenating fusion, which fail to capture complex information from multiple views. In this paper, we propose a novel fusion technique, entitled contrastive fusion, which can extract consistent representations from multiple views and maintain the characteristic of view-specific representations. Specifically, we study multi-view alignment from an information bottleneck perspective and introduce an intermediate variable to align each view-specific representation. Furthermore, we leverage a single-view clustering method as a predictive task to ensure the contrastive fusion is working. We integrate all components into an unified framework called CONtrAstive fusion Network (CONAN). Experiment results on five multi-view datasets demonstrate that CONAN outperforms state-of-the-art methods.

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Citation:

@inproceedings{ke2021conan,
  title={CONAN: Contrastive Fusion Networks for Multi-view Clustering},
  author={Ke, Guanzhou and Hong, Zhiyong and Zeng, Zhiqiang and Liu, Zeyi and Sun, Yangjie and Xie, Yannan},
  booktitle={2021 IEEE International Conference on Big Data (Big Data)},
  pages={653--660},
  year={2021},
  organization={IEEE}
}