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Jensen-Shannon Divergence analysis of pathological electrocardiogram
2012 5th International Conference on BioMedical Engineering and Informatics, 2012In this paper, complexity measure based on Jensen-Shannon Divergence was used to compute complexity of the ECG signals, which include normal sinus rhythm, atrial premature contraction (APC) and sinus bradycardia (SBR) signals from the MIT-BIH standard database. The results show that three kinds of signals have different complexity measures.
Yanting Hu, Huan Xu, Dong Hu, Jun Wang
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Non-parametric Jensen-Shannon Divergence
2015Quantifying the difference between two distributions is a common problem in many machine learning and data mining tasks. What is also common in many tasks is that we only have empirical data. That is, we do not know the true distributions nor their form, and hence, before we can measure their divergence we first need to assume a distribution or perform
Hoang Vu Nguyen, Jilles Vreeken
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Melodic Segmentation Using the Jensen-Shannon Divergence
2012 11th International Conference on Machine Learning and Applications, 2012This paper introduces an unsupervised model for melodic segmentation that extends a method initially proposed in computational biology. In the model segments are identified as sections of maximal contrast within a musical piece, using for this the Jensen-Shannon divergence.
null Marcelo +4 more
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On the Equivalence Between Jensen–Shannon Divergence and Michelson Contrast
IEEE Transactions on Information Theory, 2012This paper focuses on the link between a visible linear and local distortion in a pictorial scene and its cost in terms of information theory quantities. In particular, a formal relation between the Michelson visual contrast and the Jensen-Shannon divergence (JSD) will be provided. A universal just noticeable threshold is also derived by maximizing JSD
BRUNI, VITTORIA +2 more
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An Attributed Graph Kernel from the Jensen-Shannon Divergence
2014 22nd International Conference on Pattern Recognition, 2014Bai and Hancock recently proposed a novel information theoretic kernel for graphs, namely the Jensen-Shannon graph kernel. One drawback of their approach is that it cannot be applied to either attributed or labeled graphs. In this paper, we aim to define a new Jensen-Shannon diffusion kernel for attributed graphs.
Lu Bai 0001 +2 more
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A dissimilarity measure based on Jensen Shannon divergence measure
International Journal of General Systems, 2018ABSTRACTThe need of suitable measures to find the distance between two probability distributions arises as they play an eminent role in problems based on discrimination and inferences.
Rajesh Joshi, Satish Kumar
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Bounds for Jeffreys–Tsallis and Jensen–Shannon–Tsallis divergences
Physica A: Statistical Mechanics and its Applications, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Popescu, P. G. +2 more
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A Graph Embedding Method Using the Jensen-Shannon Divergence
2013Riesen and Bunke recently proposed a novel dissimilarity based approach for embedding graphs into a vector space. One drawback of their approach is the computational cost graph edit operations required to compute the dissimilarity for graphs. In this paper we explore whether the Jensen-Shannon divergence can be used as a means of computing a fast ...
Lu Bai 0001, Edwin R. Hancock, Lin Han
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Generalized quantum Jensen-Shannon divergence of imaginarity
Physics Letters, Section A: General, Atomic and Solid State PhysicszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yuan Sun
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A novel domain adaptation theory with Jensen–Shannon divergence
Knowledge-Based Systems, 2022Changjian Shui +5 more
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