  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}.
