Agglomerative Bregman Clustering
Machine Learning
2012-07-03 v1 Machine Learning
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.
Cite
@article{arxiv.1206.6446,
title = {Agglomerative Bregman Clustering},
author = {Matus Telgarsky and Sanjoy Dasgupta},
journal= {arXiv preprint arXiv:1206.6446},
year = {2012}
}
Comments
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)