Multi-view clustering with constraint propagation for learning with an incomplete mapping between views

Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via the partial mapping. It uses co-EM to iteratively estimate the propagation within each view based on the current clustering model, transfer the constraints across views, and update the clustering model, thereby learning a unified model for all views. We show that this approach significantly improves clustering performa...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Eric Eaton, Marie desJardins, Sara Jacob
• Keywords: CIKM 2010, Constrained Clustering, Information Technology, Multi-view Learning Algorithms, Partial Mapping