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Traditional multi-view learning approaches suffer in the presence of viewdisagreement,i.e., when samples in each view do not belong to the same classdue to view corruption, occlusion or other noise processes. In this paper wepresent a multi-view learning approach that uses a conditional entropycriterion to detect view disagreement. Once detected, samples with viewdisagreement are filtered and standard multi-view learning methods can besuccessfully applied to the remaining samples. Experimental evaluation onsynthetic and audio-visual databases demonstrates that the detection andfiltering of view disagreement considerably increases the performance oftraditional multi-view learning approaches.

Multi-View Learning in the Presence of View Disagreement