Guaranteed Bounds on Information-Theoretic Measures of Univariate Mixtures Using Piecewise Log-Sum-Exp Inequalities

Guaranteed Bounds on Information-Theoretic Measures of Univariate Mixtures Using Piecewise Log-Sum-Exp Inequalities

Information-theoretic measures, such as the entropy, the cross-entropy and the Kullback–Leibler divergence between two mixture models, are core primitives in many signal processing tasks. Since the Kullback–Leibler divergence of mixtures provably does not admit a closed-form formula, it is in practi...

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Bibliographic Details
Main Authors: Frank Nielsen, Ke Sun
Format: Article
Language:English
Published: MDPI AG 2016-12-01
Series:Entropy
Subjects:
information geometry
mixture models
α-divergences
log-sum-exp bounds
Online Access:http://www.mdpi.com/1099-4300/18/12/442

Internet

http://www.mdpi.com/1099-4300/18/12/442

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