Description: Affi nity propagation (AP) was recently introduced as an unsupervised
learning algorithm for exemplar-based clustering. We present a deriva-
tion 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 modifi cations of the standard AP
algorithm.We demonstrate this by adjusting the new AP model to repre-
sent 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.
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