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Multi-view Clustering with Constraint Propagation for Learning with an Incomplete Mapping Between Views by Eric Eaton; Marie desJardins; Sara Jacob is a scholarly article available to read on EtoBox.

What is Multi-view Clustering with Constraint Propagation for Learning with an Incomplete Mapping Between Views about?

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.

Author

Eric Eaton; Marie desJardins; Sara Jacob

Publisher

ACM

Published

2010

Language

EN