Agglomerative Bregman clustering

Matus Jan Telgarsky; Sanjoy Dasgupta

Agglomerative Bregman clustering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings of the 29th International Conference on Machine Learning, ICML 2012
Pages	1527-1534
Number of pages	8
Volume	2
State	Published - 2012
Externally published	Yes
Event	29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom Duration: Jun 26 2012 → Jul 1 2012

Other

Other	29th International Conference on Machine Learning, ICML 2012
Country	United Kingdom
City	Edinburgh
Period	6/26/12 → 7/1/12

ASJC Scopus subject areas

Human-Computer Interaction
Education

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title = "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.",

author = "Telgarsky, {Matus Jan} and Sanjoy Dasgupta",

year = "2012",

language = "English (US)",

isbn = "9781450312851",

volume = "2",

pages = "1527--1534",

booktitle = "Proceedings of the 29th International Conference on Machine Learning, ICML 2012",

note = "29th International Conference on Machine Learning, ICML 2012 ; Conference date: 26-06-2012 Through 01-07-2012",

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AU - Telgarsky, Matus Jan

AU - Dasgupta, Sanjoy

PY - 2012

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N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:84867130064

SN - 9781450312851

VL - 2

SP - 1527

EP - 1534

BT - Proceedings of the 29th International Conference on Machine Learning, ICML 2012

T2 - 29th International Conference on Machine Learning, ICML 2012

Y2 - 26 June 2012 through 1 July 2012

ER -

Agglomerative Bregman clustering

Abstract

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ASJC Scopus subject areas

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