Agglomerative Bregman Clustering
This work addresses theoretical challenges in clustering for machine learning, but appears incremental as it builds on existing Bregman divergence methods.
The paper tackles the problem of agglomerative clustering with Bregman divergences by developing geometric smoothing techniques to handle degenerate clusters and extending Bregman divergences to nondifferentiable convex functions for overcomplete exponential family models.
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.