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A scaled Bregman theorem with applications

Nock, Richard; Menon, Aditya; Ong, Cheng Soon

Description

Bregman divergences play a central role in the design and analysis of a range of machine learning algorithms through a handful of popular theorems. We present a new theorem which shows that "Bregman distortions" (employing a potentially non-convex generator) may be exactly re-written as a scaled Bregman divergence computed over transformed data. This property can be viewed from the standpoints of geometry (a scaled isometry with adaptive metrics) or convex optimization (relating generalized...[Show more]

CollectionsANU Research Publications
Date published: 2016
Type: Conference paper
URI: http://hdl.handle.net/1885/154047
Source: Advances in Neural Information Processing Systems
Access Rights: Open Access

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