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Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization

Abstract : We tackle the issue of classifier combinations when observations have multiple views. Our method jointly learns view-specific weighted majority vote classifiers (i.e. for each view) over a set of base voters, and a second weighted majority vote classifier over the set of these view-specific weighted majority vote classifiers. We show that the empirical risk minimization of the final majority vote given a multiview training set can be cast as the minimization of Bregman divergences. This allows us to derive a parallel-update optimization algorithm for learning our multiview model. We empirically study our algorithm with a particular focus on the impact of the training set size on the multiview learning results. The experiments show that our approach is able to overcome the lack of labeled information.
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Contributor : Anil Goyal <>
Submitted on : Thursday, May 24, 2018 - 1:58:03 PM
Last modification on : Monday, April 20, 2020 - 11:24:02 AM
Document(s) archivé(s) le : Saturday, August 25, 2018 - 2:25:47 PM

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  • HAL Id : hal-01799173, version 1
  • ARXIV : 1805.10212

Citation

Anil Goyal, Emilie Morvant, Massih-Reza Amini. Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization. International Symposium on Intelligent Data Analysis (IDA), Oct 2018, ‘s-Hertogenbosch, Netherlands. ⟨hal-01799173⟩

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