Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters

Code

• github.com/goyalanil/PB-MVBoost
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• github.com/goyalanil/Multiview_Dataset_MNIST
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Abstract

In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information; and ii) weights over views by optimizing a PAC-Bayes multiview C-Bound that takes into account the accuracy of view-specific classifiers and the diversity between the views. We derive a generalization bound for this strategy following the PAC-Bayes theory which is a suitable tool to deal with models expressed as weighted combination over a set of voters. Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.

Tasks

Reproductions