title: A binary variable model for affinity propagation.
abstract: Affinity propagation (AP) was recently introduced as an unsupervised learning algorithm for exemplar-based clustering. We present a derivation of AP that is much simpler than the original one and is based on a quite different graphical model. The new model allows easy derivations of message updates for extensions and modifications of the standard AP algorithm. We demonstrate this by adjusting the new AP model to represent the capacitated clustering problem. For those wishing to investigate or extend the graphical model of the AP algorithm, we suggest using this new formulation since it allows a simpler and more intuitive model manipulation.
authors.affiliation: Probabilistic and Statistical Inference Group, Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Canada. inmar@psi.toronto.edu
authors.forename: Inmar E
authors.initials: IE
authors.lastname: Givoni
authors.forename: Brendan J
authors.initials: BJ
authors.lastname: Frey
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D010363:Pattern Recognition, Automated