By "intelligently" fusing the complementary information across different views, multi-view learning is able to improve the performance of classification tasks. In this work, we extend the information bottleneck principle to a supervised multi-view learning scenario and use the recently proposed matrix-based R{\'e}nyi's α-order entropy functional to optimize the resulting objective directly, without the necessity of variational approximation or adversarial training. Empirical results in both synthetic and real-world datasets suggest that our method enjoys improved robustness to noise and redundant information in each view, especially given limited training samples. Code is available at~\url{https://github.com/archy666/MEIB}.
@article{arxiv.2204.10530,
title = {Multi-view Information Bottleneck Without Variational Approximation},
author = {Qi Zhang and Shujian Yu and Jingmin Xin and Badong Chen},
journal= {arXiv preprint arXiv:2204.10530},
year = {2022}
}