Multi-view learning in the presence of view disagreement

C. Mario Christoudias, Raquel Urtasun, Trevor Darrell
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:88-96, 2008.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR6-christoudias08a, title = {Multi-view learning in the presence of view disagreement}, author = {Christoudias, C. Mario and Urtasun, Raquel and Darrell, Trevor}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {88--96}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/christoudias08a/christoudias08a.pdf}, url = {https://proceedings.mlr.press/r6/christoudias08a.html}, 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.}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T Multi-view learning in the presence of view disagreement %A C. Mario Christoudias %A Raquel Urtasun %A Trevor Darrell %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-christoudias08a %I PMLR %P 88--96 %U https://proceedings.mlr.press/r6/christoudias08a.html %V R6 %X 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. %Z Reissued by PMLR on 09 October 2024.
APA
Christoudias, C.M., Urtasun, R. & Darrell, T.. (2008). Multi-view learning in the presence of view disagreement. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:88-96 Available from https://proceedings.mlr.press/r6/christoudias08a.html. Reissued by PMLR on 09 October 2024.

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