Results 11 to 20 of about 4,244 (108)
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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Entropy-Based Uncertainty-Aware Exploratory Factor Analysis for Ordinal Data: Application to Tramway Cultural Tourism Evaluation [PDF]
EntropyBackground: Perception-based evaluation using Likert-scale survey data is widely applied in tourism and transport research, yet conventional point-valued encoding imposes artificial precision and overlooks ambiguity between adjacent ordinal categories ...
Jiaozi Pu, Yaxin Shi
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Entropy, 2021 We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by considering a variational definition with respect to a generic mean, thereby extending the notion of Sibson’s information radius.
Frank Nielsen
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Entropy, 2021 In this work, we first consider the discrete version of Fisher information measure and then propose Jensen–Fisher information, to develop some associated results.
Omid Kharazmi +1 more
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Quantifying the Dissimilarity of Texts
Information, 2023 Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering.
Benjamin Shade, Eduardo G. Altmann
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Refined Young Inequality and Its Application to Divergences
Entropy, 2021 We give bounds on the difference between the weighted arithmetic mean and the weighted geometric mean. These imply refined Young inequalities and the reverses of the Young inequality. We also studied some properties on the difference between the weighted
Shigeru Furuichi, Nicuşor Minculete
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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
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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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Technologies, 2021 The performance of a free-space optical (FSO) communications link suffers from the deleterious effects of weather conditions and atmospheric turbulence. In order to better estimate the reliability and availability of an FSO link, a suitable distribution ...
Antonios Lionis +3 more
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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
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