@conference {13456,
	title = {Multi-view clustering with constraint propagation for learning with an incomplete mapping between views},
	booktitle = {Proceedings of the 19th ACM international conference on Information and knowledge management},
	series = {CIKM {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {389 - 398},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	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.},
	keywords = {constrained clustering, multi-view learning, semi-supervised learning},
	isbn = {978-1-4503-0099-5},
	doi = {10.1145/1871437.1871489},
	url = {http://doi.acm.org/10.1145/1871437.1871489},
	author = {Eaton,Eric and desJardins, Marie and Jacob,Sara}
}
