On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means [PDF]
Entropy, 2019 The Jensen–Shannon divergence is a renowned bounded symmetrization of the unbounded Kullback–Leibler divergence which measures the total Kullback–Leibler divergence to the average mixture distribution.
Frank Nielsen
exaly +5 more sources
Two Types of Geometric Jensen–Shannon Divergences [PDF]
EntropyThe geometric Jensen–Shannon divergence (G-JSD) has gained popularity in machine learning and information sciences thanks to its closed-form expression between Gaussian distributions.
Frank Nielsen
doaj +5 more sources
On the Jensen-Shannon divergence and the variation distance for categorical probability distributions [PDF]
Kybernetika, 2021 summary:We establish a decomposition of the Jensen-Shannon divergence into a linear combination of a scaled Jeffreys' divergence and a reversed Jensen-Shannon divergence. Upper and lower bounds for the Jensen-Shannon divergence are then found in terms of
Corander, Jukka +2 more
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Properties of classical and quantum Jensen-Shannon divergence [PDF]
Physical Review A, 2009 Jensen-Shannon divergence (JD) is a symmetrized and smoothed version of the most important divergence measure of information theory, Kullback divergence.
Harremoës, P. (Peter), Briët, J. (Jop)
core +5 more sources
Multimodal Generative Learning Utilizing Jensen-Shannon Divergence [PDF]
CoRR, 2019 Learning from different data types is a long-standing goal in machine learning research, as multiple information sources co-occur when describing natural phenomena.
Vogt, Julia E. +3 more
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The Metric Property of the Quantum Jensen-Shannon Divergence [PDF]
Advances in Mathematics, 2021 In this short note, we prove that the square root of the quantum Jensen-Shannon divergence is a true metric on the cone of positive matrices, and hence in particular on the quantum state ...
Virosztek, Daniel ; https://orcid.org/ +1 more
core +6 more sources
Metrization of powers of the Jensen-Shannon divergence [PDF]
Kybernetikasummary:Metrization of statistical divergences is valuable in both theoretical and practical aspects. One approach to obtaining metrics associated with divergences is to consider their fractional powers.
Okamura, Kazuki
core +3 more sources
A method for continuous-range sequence analysis with Jensen-Shannon divergence
Papers in Physics, 2021 Mutual Information (MI) is a useful Information Theory tool for the recognition of mutual dependence between data sets. Several methods have been developed fore estimation of MI when both data sets are of the discrete type or when both are of the ...
Miguel Ángel Ré +1 more
doaj +5 more sources
Some bounds for skewed α-Jensen-Shannon divergence
Results in Applied Mathematics, 2019 Based on the skewed Kullback-Leibler divergence introduced in the natural language processing, we derive the upper and lower bounds on the skewed version of the Jensen-Shannon divergence and investigate properties of them.
Takuya Yamano
doaj +2 more sources
Granger Causality and Jensen–Shannon Divergence to Determine Dominant Atrial Area in Atrial Fibrillation [PDF]
Entropy, 2018 Atrial fibrillation (AF) is already the most commonly occurring arrhythmia. Catheter pulmonary vein ablation has emerged as a treatment that is able to make the arrhythmia disappear; nevertheless, recurrence to arrhythmia is very frequent. In this study,
Raquel Cervigón +4 more
doaj +2 more sources