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arXiv · 2411.06276

Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds

Mehdi Hennequin·Abdelkrim Zitouni·Khalid Benabdeslem·Haytham Elghazel·Yacine Gaci
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Abstract

The PAC-Bayesian framework has significantly advanced the understanding of statistical learning, particularly for majority voting methods. Despite its successes, its application to multi-view learning -- a setting with multiple complementary data representations -- remains underexplored. In this work, we extend PAC-Bayesian theory to multi-view learning, introducing novel generalization bounds based on R\'enyi divergence. These bounds provide an alternative to traditional Kullback-Leibler divergence-based counterparts, leveraging the flexibility of R\'enyi divergence. Furthermore, we propose first- and second-order oracle PAC-Bayesian bounds and extend the C-bound to multi-view settings. To bridge theory and practice, we design efficient self-bounding optimization algorithms that align with our theoretical results.

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Mehdi Hennequin, Abdelkrim Zitouni, Khalid Benabdeslem, Haytham Elghazel, Yacine Gaci. 2024-11-09. Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds. https://arxiv.org/abs/2411.06276

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