Article

Multi-view learning in the presence of view disagreement

Authors:

C. Mario Christoudias,

Raquel Urtasun,

Trevor DarrellAuthors Info & Claims

UAI'08: Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence

Pages 88 - 96

Published: 09 July 2008 Publication History

Abstract

Traditional multi-view learning approaches suffer in the presence of view disagreement, i.e., when samples in each view do not belong to the same class due to view corruption, occlusion or other noise processes. In this paper we present a multi-view learning approach that uses a conditional entropy criterion to detect view disagreement. Once detected, samples with view disagreement are filtered and standard multi-view learning methods can be successfully applied to the remaining samples. Experimental evaluation on synthetic and audio-visual databases demonstrates that the detection and filtering of view disagreement considerably increases the performance of traditional multi-view learning approaches.

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Cited By

Liang PCheng YFan XLing CNie SChen RDeng ZAllen NAuerbach RMahmood FSalakhutdinov RMorency LOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Quantifying & modeling multimodal interactionsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667314(27351-27393)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667314
Liang PCheng YSalakhutdinov RMorency L(2023)Multimodal Fusion Interactions: A Study of Human and Automatic QuantificationProceedings of the 25th International Conference on Multimodal Interaction10.1145/3577190.3614151(425-435)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3577190.3614151
Gong FNie YXu HAl Hasan MXiong L(2022)Gromov-Wasserstein Multi-modal Alignment and ClusteringProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557339(603-613)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557339
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Multi-view learning in the presence of view disagreement
1. Computing methodologies
  1. Machine learning

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Published In

cover image Guide Proceedings

UAI'08: Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence

July 2008

609 pages

ISBN:0974903949

Editors:
David McAllester,
Petri Myllymaki
Complex Systems Helsinki Institute, Helsinki, Finland

Publisher

AUAI Press

Arlington, Virginia, United States

Publication History

Published: 09 July 2008

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Cited By

Liang PCheng YFan XLing CNie SChen RDeng ZAllen NAuerbach RMahmood FSalakhutdinov RMorency LOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Quantifying & modeling multimodal interactionsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667314(27351-27393)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667314
Liang PCheng YSalakhutdinov RMorency L(2023)Multimodal Fusion Interactions: A Study of Human and Automatic QuantificationProceedings of the 25th International Conference on Multimodal Interaction10.1145/3577190.3614151(425-435)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3577190.3614151
Gong FNie YXu HAl Hasan MXiong L(2022)Gromov-Wasserstein Multi-modal Alignment and ClusteringProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557339(603-613)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557339
Baltrusaitis TAhuja CMorency L(2019)Multimodal Machine LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2018.279860741:2(423-443)Online publication date: 10-Dec-2019
https://dl.acm.org/doi/10.1109/TPAMI.2018.2798607
Zhuang FKarypis GNing XHe QShi Z(2019)Multi-view learning via probabilistic latent semantic analysisInformation Sciences: an International Journal10.1016/j.ins.2012.02.058199(20-30)Online publication date: 6-Jan-2019
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Baltrušaitis TAhuja CMorency L(2018)Challenges and applications in multimodal machine learningThe Handbook of Multimodal-Multisensor Interfaces10.1145/3107990.3107993(17-48)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1145/3107990.3107993
Zhang LSrisukkham WNeoh SLim CPandit D(2018)Classifier ensemble reduction using a modified firefly algorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.10.00193:C(395-422)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.eswa.2017.10.001
Iwata TYamada M(2016)Multi-view anomaly detection via robust probabilistic latent variable modelsProceedings of the 30th International Conference on Neural Information Processing Systems10.5555/3157096.3157224(1144-1152)Online publication date: 5-Dec-2016
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Yao GZeng HChao FSu CLin CZhou C(2016)Integration of classifier diversity measures for feature selection-based classifier ensemble reductionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1927-720:8(2995-3005)Online publication date: 1-Aug-2016
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Tuyls KWeiss G(2012)Multiagent LearningAI Magazine10.1609/aimag.v33i3.242633:3(41-52)Online publication date: 1-Sep-2012
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