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

TL;DR

This paper introduces PB-MVBoost, a multiview boosting algorithm that optimizes view-specific classifier weights and view diversity using PAC-Bayes bounds, demonstrating improved performance on public datasets.

Contribution

The paper presents a novel multiview boosting method that controls diversity and accuracy of view-specific voters through PAC-Bayes theory, advancing multiview learning techniques.

Findings

Outperforms state-of-the-art models on three datasets

Effectively balances view diversity and classifier accuracy

Provides theoretical generalization bounds for the approach

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.