Agglomerative Bregman clustering

Matus Jan Telgarsky, Sanjoy Dasgupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Pages1527-1534
Number of pages8
Volume2
StatePublished - 2012
Externally publishedYes
Event29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom
Duration: Jun 26 2012Jul 1 2012

Other

Other29th International Conference on Machine Learning, ICML 2012
CountryUnited Kingdom
CityEdinburgh
Period6/26/127/1/12

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

Fingerprint Dive into the research topics of 'Agglomerative Bregman clustering'. Together they form a unique fingerprint.

Cite this