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CIKM
2010
Springer

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

9 years 2 days ago
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...
Eric Eaton, Marie desJardins, Sara Jacob
Added 24 Jan 2011
Updated 24 Jan 2011
Type Journal
Year 2010
Where CIKM
Authors Eric Eaton, Marie desJardins, Sara Jacob
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