MULTI-VIEW INFORMATION BOTTLENECK WITHOUT VARIATIONAL APPROXIMATION

By "intelligently" fuse the complementary information across different views, multi-view learning is able to improve the performance of classification task. In this work, we extend the information bottleneck principle to supervised multi-view learning scenario and use the recently proposed matrix-based Renyi?s ?-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 https://github.com/archy666/MEIB.