The extended Kullback-Leibler divergence measure in the unknown probability density function cases and applications
by Hoa Le; Hoang Van Truong; Pham The Bao
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 14, No. 4, 2021

Abstract: The Kullback-Leibler divergence measure is used to evaluate the similarity between two probability distributions. In theory, the probability density functions are known before applying the formula. However, estimating this information of real data is challenging. For that reason, the Kullback-Leibler divergence needs to be modified for similarity measures in these cases. In this paper, we proposed and evaluated an extended Kullback-Leibler divergence similarity measure by two experiments. The first experiment is based on two datasets that have unknown probability density functions, while the second one is conducted on one dataset with an unknown probability density function and the other with a known probability density function. Besides, the proposed method is applied to the simulated data and the plagiarism detection cases.

Online publication date: Thu, 28-Oct-2021

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