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 $\alpha$-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}.

翻译：通过“明智地”在不同观点中传播补充信息,多视角学习能够改善分类任务的业绩。在这项工作中,我们将信息瓶颈原则推广到监督的多视角学习情景,并使用最近提议的基于矩阵的R'e}nyi $\ alpha$-serve entropy 功能,直接优化由此产生的目标,而不必进行变式近似或对抗性培训。合成和现实世界数据集的经验性结果表明,我们的方法对每种观点中的噪音和多余信息都更加可靠,特别是考虑到有限的培训样本。代码可在<url{https://github.com/archy666/MEIB}查阅。