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Monoparametric family of metrics derived from classical Jensen–Shannon divergence

Osán, Tristán Martín et al · Elsevier Science · 2018

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Jensen–Shannon divergence is a well known multi-purpose measure of dissimilarity between probability distributions. It has been proven that the square root of this quantity is a true metric in the sense that, in addition to the basic properties of a distance, it also satisfies the triangle inequality. In this work we extend this last result to prove that in fact it is possible to derive a monoparametric family of metrics from the classical Jensen–Shannon divergence. Motivated by our results, an application into the field of symbolic sequences segmentation is explored. Additionally, we analyze the possibility to extend this result into the quantum realm. Fil: Osán, Tristán Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina Fil: Bussandri, Diego. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física. Sección Física; Argentina

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APA 7

Osán, T. M. E. A. (2018). Monoparametric family of metrics derived from classical Jensen–Shannon divergence. http://hdl.handle.net/11336/65552

MLA

Osán, Tristán Martín et al. "Monoparametric family of metrics derived from classical Jensen–Shannon divergence." 2018. http://hdl.handle.net/11336/65552.

Chicago

Osán, Tristán Martín et al. 2018. "Monoparametric family of metrics derived from classical Jensen–Shannon divergence.". http://hdl.handle.net/11336/65552.

Harvard

Osán, T. M. E. A. 2018, Monoparametric family of metrics derived from classical Jensen–Shannon divergence, Elsevier Science, available at: http://hdl.handle.net/11336/65552 [Accessed 6 Aug. 2026].

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Title
Monoparametric family of metrics derived from classical Jensen–Shannon divergence
Author / contributors
Osán, Tristán Martín et al
Publisher
Elsevier Science
Publication year
2018
ISSN
0378-4371
ISSN
0378-4371
Language
English

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