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Interpretable Sensor Change Detection via Conditional Cauchy-Schwarz Divergence. [PDF]
Sensors (Basel)Wang W, Shen Y, Ni Y, Wu W.
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A decision cognizant Kullback–Leibler divergence
Pattern Recognition, 2017 In decision making systems involving multiple classifiers there is the need to assess classifier (in)congruence, that is to gauge the degree of agreement between their outputs. A commonly used measure for this purpose is the Kullback–Leibler (KL) divergence. We propose a variant of the KL divergence, named decision cognizant Kullback–Leibler divergence
Moacir Ponti +2 more
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Kullback–Leibler Divergence Metric Learning
IEEE Transactions on Cybernetics, 2022The Kullback-Leibler divergence (KLD), which is widely used to measure the similarity between two distributions, plays an important role in many applications. In this article, we address the KLD metric-learning task, which aims at learning the best KLD-type metric from the distributions of datasets.
Shuyi Ji +5 more
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The fractional Kullback–Leibler divergence
Journal of Physics A: Mathematical and Theoretical, 2021Abstract The Kullback–Leibler divergence or relative entropy is generalised by deriving its fractional form. The conventional Kullback–Leibler divergence as well as other formulations emerge as special cases. It is shown that the fractional divergence encapsulates different relative entropy states via the manipulation of the ...
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Kullback-Leibler Divergence Revisited
Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, 2017Thee KL divergence is the most commonly used measure for comparing query and document language models in the language modeling framework to ad hoc retrieval. Since KL is rank equivalent to a specific weighted geometric mean, we examine alternative weighted means for language-model comparison, as well as alternative divergence measures.
Fiana Raiber, Oren Kurland
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Distributions of the Kullback–Leibler divergence with applications
British Journal of Mathematical and Statistical Psychology, 2011The Kullback–Leibler divergence (KLD) is a widely used method for measuring the fit of two distributions. In general, the distribution of the KLD is unknown. Under reasonable assumptions, common in psychometrics, the distribution of the KLD is shown to be asymptotically distributed as a scaled (non‐central) chi‐square with one ...
Belov, Dmitry I., Armstrong, Ronald D.
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The Kullback-Leibler Divergence and Nonnegative Matrices
IEEE Transactions on Information Theory, 2006This correspondence establishes an interesting connection between the Kullback-Leibler divergence and the Perron root of nonnegative irreducible matrices. In the second part of the correspondence, we apply these results to the power control problem in wireless communications networks to show a fundamental tradeoff between fairness and efficiency.
Holger Boche, Slawomir Stanczak
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Kullback-Leibler Divergence-Based Visual Servoing
2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2021This paper proposes a Kullback-Leibler (K-L) divergence-based visual servoing scheme. K-L divergence, also known as relative entropy, is a measure of the difference between two probability distributions. By employing the K-L divergence as a new error metric to evaluate the similarity between the actual and desired images, and then formulating the ...
Xiangfei Li +2 more
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