Multi-view Information Bottleneck Without Variational Approximation

• URL: http://arxiv.org/abs/2204.10530v1
• Date: Fri, 22 Apr 2022 06:48:04 GMT
• Title: Multi-view Information Bottleneck Without Variational Approximation
• Authors: Qi Zhang, Shujian Yu, Jingmin Xin, Badong Chen
• Abstract summary: We extend the information bottleneck principle to a supervised multi-view learning scenario. We use the recently proposed matrix-based R'enyi's $alpha$-order entropy functional to optimize the resulting objective. Empirical results in both synthetic and real-world datasets suggest that our method enjoys improved robustness to noise and redundant information in each view.
• Score: 34.877573432746246
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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}.

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