PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On the statistical estimation of Rényi entropies
Elia Liitiainen, Amaury Lendasse and Francesco Corona
In: Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE International Workshop on, 1-4 Sept. 2009, Grnoble, France.

Abstract

Estimating entropies is important in many fields including statistical physics, machine learning and statistics. While the Shannon logarithmic entropy is the most fundamental, other Reacutenyi entropies are also of importance. In this paper, we derive a bias corrected estimator for a subset of Renyi entropies. The advantage of the estimator is demonstrated via theoretical and experimental considerations.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6662
Deposited By:Amaury Lendasse
Deposited On:08 March 2010