Risk-based generalizations of f-divergences

Darío García-García*, Ulrike Von Luxburg, Raúl Santos-Rodríguez

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

8 Citations (Scopus)

Abstract

We derive a generalized notion of f-divergences, called (f,l)-divergences. We show that this generalization enjoys many of the nice properties of/-divergences, although it is a richer family. It also provides alternative definitions of standard divergences in terms of surrogate risks. As a first practical application of this theory, we derive a new estimator for the Kulback-Leibler divergence that we use for clustering sets of vectors.

Original languageEnglish
Title of host publicationProceedings of the 28th International Conference on Machine Learning, ICML 2011
Pages417-424
Number of pages8
Publication statusPublished - 7 Oct 2011
Event28th International Conference on Machine Learning, ICML 2011 - Bellevue, WA, United Kingdom
Duration: 28 Jun 20112 Jul 2011

Conference

Conference28th International Conference on Machine Learning, ICML 2011
CountryUnited Kingdom
CityBellevue, WA
Period28/06/112/07/11

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