Agglomerative Bregman Clustering

Telgarsky, Matus, Dasgupta, Sanjoy

arXiv.org Machine Learning 

This manuscript develops the theory of agglomerative clustering with Bregman divergences. Geometric smoothing techniques are developed to deal with degenerate clusters. To allow for cluster models based on exponential families with overcomplete representations, Bregman divergences are developed for nondifferentiable convex functions.

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