Agglomerative Bregman Clustering

Abstract

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.

Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)

References in corpus (1)

• Bayesian Rose Trees

Cited by in corpus (2)

• The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking
• Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility