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Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure

Published on Feb 1, 2016in IEEE Transactions on Signal Processing5.23
· DOI :10.1109/TSP.2015.2477805
Visar Berisha12
Estimated H-index: 12
(ASU: Arizona State University),
Alan Wisler4
Estimated H-index: 4
(ASU: Arizona State University)
+ 1 AuthorsAndreas Spanias28
Estimated H-index: 28
(ASU: Arizona State University)
Sources
Abstract
Information divergence functions play a critical role in statistics and information theory. In this paper we show that a nonparametric f -divergence measure can be used to provide improved bounds on the minimum binary classification probability of error for the case when the training and test data are drawn from the same distribution and for the case where there exists some mismatch between training and test distributions. We confirm these theoretical results by designing feature selection algorithms using the criteria from these bounds and by evaluating the algorithms on a series of pathological speech classification tasks.
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The problem of f-divergence estimation is important in the fields of machine learning, information theory, and statistics. While several nonparametric divergence estimators exist, relatively few have known convergence properties. In particular, even for those estimators whose MSE convergence rates are known, the asymptotic distributions are unknown. We establish the asymptotic normality of a recently proposed ensemble estimator of f-divergence between two distributions from a finite number of sa...
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