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Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization

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Anil Goyal (AMA, LHC), Emilie Morvant (LHC), Massih-Reza Amini (AMA)

Machine Learning · Submitted 25 May 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The experiments show that our approach is able to overcome the lack of labeled information.

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arXiv ID1805.10212 (version 1)
Pith IDG7LCRQNG, Pith's permanent record of this paper and its review
CitationAnil Goyal (AMA, Emilie Morvant (LHC), LHC), Massih-Reza Amini (AMA). "Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization." arXiv:1805.10212 (2018). https://pith.science/paper/G7LCRQNG
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What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.18 · human participants 0.05 · controlled comparison 0.08 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.10 · mentions code or data availability 0.02.

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arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

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#sentencerolethe authors' own workstates the main conclusionstrength
s0 We tackle the issue of classifier combinations when observations have multiple views. background 0.95 0.31 0.03 asserted 0.65
s1 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. method 0.92 0.91 0.10 asserted 0.96
s2 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. contribution 0.36 0.93 0.23 asserted 1.00
s3 This allows us to derive a parallel-update optimization algorithm for learning our multiview model. method 0.92 0.94 0.07 asserted 0.96
s4 We empirically study our algorithm with a particular focus on the impact of the training set size on the multiview learning results. contribution 0.55 0.96 0.04 asserted 0.81
s5 The experiments show that our approach is able to overcome the lack of labeled information. result 0.96 0.95 0.53 asserted 0.95

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.95.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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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