Agglomerative Bregman Clustering

2012/06/27 by Matus Telgarsky, Telgarsky, Matus, Sanjoy Dasgupta +1
Computer Science · Mathematics · #Advanced Clustering Algorithms Research #Bayesian Methods and Mixture Models #Data Management and Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1206.6446

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

arxiv created 2012/06/27 · openalex publication_date 2012/06/27 · arxiv updated 2012/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

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