  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.
