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Extending the Affinity Propagation Model

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If you have a question about this talk, please contact Zoubin Ghahramani.

Affinity Propagation (AP, Frey & Dueck, 2007) is an exemplar-based clustering algorithm that is based on loopy belief-propagation on a particular factor-graph. In my talk I will present an alternative formulation and derivation of AP that lends itself nicely to modifications and extensions of the basic algorithm. In particular, I will discuss a semi-supervised variant of Affinity Propagation that uses equivalence constraints, and demonstrate its applicability to user interactive image segmentation. Finally, I will describe some on-going work regarding interesting links between the alternative AP formulation and some graph-theoretical problems such as bi-partite weighted matching, which lead us to believe it may be possible to establish convergence criteria for AP in terms of solutions obtained by linear programming relaxations.

This talk is part of the Machine Learning @ CUED series.

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