Feb 19, 2018 · The most popular estimator is the one proposed by Kraskov, Stögbauer, and Grassberger (KSG) in 2004 and is nonparametric and based on the ...
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The k-nearest neighbors (KNN) algorithm is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions.
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Nov 22, 2017 · An exhaustive numerical study shows that the discussed k-NN estimators perform well even for relatively small number of samples (few thousands).
Nov 22, 2017 · This report studies data-driven estimation of the directed information (DI) measure between twoem discrete-time and continuous-amplitude random ...
Abstract—In this paper, we develop a universal algorithm to estimate Massey's directed information for stationary ergodic processes.
Feb 8, 2024 · Abstract—This work develops a new method for estimating and optimizing the directed information rate between two jointly.
Mar 22, 2018 · We develop a g-knn estimator of entropy and mutual information based on elliptical volume elements, capturing the local stretching and compression.
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We prove finite sample bounds for k-nearest neighbor (k-NN) density estimation, and subsequently apply these bounds to the related problem of mode ...
In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951.
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Mar 30, 2021 · Importantly, implementation of DI estimation via the KL-divergence has been shown to scale very well with dimensionality in the con- tinuously ...