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arXiv · 1606.07240

PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach

Anil Goyal·Emilie Morvant·Pascal Germain·Massih-Reza Amini
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Abstract

We study a two-level multiview learning with more than two views under the PAC-Bayesian framework. This approach, sometimes referred as late fusion, consists in learning sequentially multiple view-specific classifiers at the first level, and then combining these view-specific classifiers at the second level. Our main theoretical result is a generalization bound on the risk of the majority vote which exhibits a term of diversity in the predictions of the view-specific classifiers. From this result it comes out that controlling the trade-off between diversity and accuracy is a key element for multiview learning, which complements other results in multiview learning. Finally, we experiment our principle on multiview datasets extracted from the Reuters RCV1/RCV2 collection.

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Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini. 2016-06-23. PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach. https://arxiv.org/abs/1606.07240

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