On a Generalization of the Jensen–Shannon Divergence and the Jensen–Shannon Centroid [PDF]
Entropy, 2020 The Jensen−Shannon divergence is a renown bounded symmetrization of the Kullback−Leibler divergence which does not require probability densities to have matching supports. In this paper, we introduce a vector-skew generalization of the scalar
Frank Nielsen
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Tight Bounds Between the Jensen–Shannon Divergence and the Minmax Divergence [PDF]
EntropyMotivated by questions arising at the intersection of information theory and geometry, we compare two dissimilarity measures between finite categorical distributions.
Arseniy Akopyan +3 more
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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
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Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence [PDF]
SensorsThe proliferation of radio frequency (RF) devices in contemporary society, especially in the fields of smart homes, Internet of Things (IoT) gadgets, and smartphones, underscores the urgent need for robust identification methods to strengthen ...
Rene Francisco Santana-Cruz +4 more
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Fault Detection Based on Multi-Dimensional KDE and Jensen–Shannon Divergence [PDF]
Entropy, 2021 Weak fault signals, high coupling data, and unknown faults commonly exist in fault diagnosis systems, causing low detection and identification performance of fault diagnosis methods based on T2 statistics or cross entropy. This paper proposes a new fault
Juhui Wei +4 more
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Learning geometric Jensen-Shannon divergence for tiny object detection in remote sensing images [PDF]
Frontiers in Neurorobotics, 2023 Tiny objects in remote sensing images only have a few pixels, and the detection difficulty is much higher than that of regular objects. General object detectors lack effective extraction of tiny object features, and are sensitive to the Intersection-over-
Shuyan Ni +7 more
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Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence [PDF]
Frontiers in Oncology, 2021 MotivationThe evolution of complex diseases can be modeled as a time-dependent nonlinear dynamic system, and its progression can be divided into three states, i.e., the normal state, the pre-disease state and the disease state.
Jinling Yan +7 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.
A. F. T. Martins +20 more
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On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means
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
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Noise Reduction Using Singular Value Decomposition with Jensen–Shannon Divergence for Coronary Computed Tomography Angiography [PDF]
Diagnostics, 2023 Coronary computed tomography angiography (CCTA) is widely used due to its improvements in computed tomography (CT) diagnostic performance. Unlike other CT examinations, CCTA requires shorter rotation times of the X-ray tube, improving the temporal ...
Ryosuke Kasai, Hideki Otsuka
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