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ICASSP
2011
IEEE

Entropy estimation using the principle of maximum entropy

9 years 3 months ago
Entropy estimation using the principle of maximum entropy
In this paper, we present a novel entropy estimator for a given set of samples drawn from an unknown probability density function (PDF). Counter to other entropy estimators, the estimator presented here is parametric. The proposed estimator uses the maximum entropy principle to offer an m-term approximation to the underlying distribution and does not rely on local density estimation. The accuracy of the proposed algorithm is analyzed and it is shown that the estimation error is ≤ O( log n/n). In addition to the analytic results, a numerical evaluation of the estimator on synthetic data as well as on experimental sensor network data is provided. We demonstrate a significant improvement in accuracy relative to other methods.
Behrouz Behmardi, Raviv Raich, Alfred O. Hero
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Behrouz Behmardi, Raviv Raich, Alfred O. Hero
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