Multi-view Spectral Clustering on Conflicting Views

In a growing number of application domains, multiple feature representations or views are available to describe objects. Multi-view clustering tries to find similar groups of objects across these views. This task is complicated when the corresponding clusterings in each view show poor agreement (conflicting views). In such cases, traditional multi-view clustering methods will not benefit from using multi-view data. Here, we propose to overcome this problem by combining the ideas of multi-view spectral clustering with alternative clustering through kernel-based dimensionality reduction. Our method automatically determines feature transformations in each view that lead to an optimal clustering w.r.t to a new proposed objective function for conflicting views. In our experiments, our approach outperforms state-of-the-art multi-view clustering methods by more accurately detecting the ground truth clustering supported by all views.