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

Authors:
Eric Eaton;Marie desJardins;Sara Jacob
Affiliations:
Bryn Mawr College, Bryn Mawr, PA, USA;University of Maryland Baltimore County, Baltimore, MD, USA;Lockheed Martin Advanced Technology Laboratories, Cherry Hill, NJ, USA
Venue:
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Year:
2010

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Abstract

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 performance over several other methods for transferring constraints and allows multi-view clustering to be reliably applied when given a limited mapping between the views.