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Divergence measures based on the Shannon entropy (1991)
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| Venue: | IEEE Transactions on Information theory |
| Citations: | 666 - 0 self |
BibTeX
@ARTICLE{Lin91divergencemeasures,
author = {Jianhua Lin},
title = {Divergence measures based on the Shannon entropy},
journal = {IEEE Transactions on Information theory},
year = {1991},
volume = {37},
pages = {145--151}
}
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Abstract
Abstract-A new class of information-theoretic divergence measures based on the Shannon entropy is introduced. Unlike the well-known Kullback divergences, the new measures do not require the condition of absolute continuity to be satisfied by the probability distributions in-volved. More importantly, their close relationship with the variational distance and the probability of misclassification error are established in terms of bounds. These bounds are crucial in many applications of divergence measures. The new measures are also well characterized by the properties of nonnegativity, finiteness, semiboundedness, and boundedness. Index Terms-Divergence, dissimilarity measure, discrimination in-formation, entropy, probability of error bounds. I.
Keyphrases
divergence measure shannon entropy new measure information-theoretic divergence measure dissimilarity measure abstract-a new class error bound well-known kullback divergence close relationship many application misclassification error index terms-divergence discrimination in-formation variational distance absolute continuity
