Multi-View Learning in the Presence of View Disagreement
This work addresses a specific issue in multi-view learning for applications like audio-visual data processing, but it is incremental as it builds on existing methods by adding a filtering step.
The paper tackles the problem of view disagreement in multi-view learning, where samples in each view may not belong to the same class due to corruption or noise, by proposing a method that uses conditional entropy to detect and filter such samples, resulting in a considerable performance increase for traditional multi-view learning approaches as demonstrated on synthetic and audio-visual databases.
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.