An approach based on Chernoff distance to sparse sensing for distributed detection

Dong, Jiaxue; Niu, Dunbiao; Song, Enbin

doi:10.23919/icif.2017.8009840

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“…In statistical signal processing, information fusion and machine learning, one often considers the skew Bhattacharryya distance [13,14,15] or the Chernoff distance [16,17,18] for exponential families (e.g., Gaussian/multinoulli): This highlights the important role in disguise of the equivalent skew Jensen divergences (see Eq. 12).…”

Section: Introductionmentioning

confidence: 99%

A generalization of the Jensen divergence: The chord gap divergence

Nielsen

2017

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We introduce a novel family of distances, called the chord gap divergences, that generalizes the Jensen divergences (also called the Burbea-Rao distances), and study its properties. It follows a generalization of the celebrated statistical Bhattacharyya distance that is frequently met in applications. We report an iterative concave-convex procedure for computing centroids, and analyze the performance of the k-means++ clustering with respect to that new dissimilarity measure by introducing the Taylor-Lagrange remainder form of the skew Jensen divergences.

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Section: Introductionmentioning

confidence: 99%

A generalization of the Jensen divergence: The chord gap divergence

Nielsen

2017

Preprint

View full text Add to dashboard Cite

show abstract

An approach based on Chernoff distance to sparse sensing for distributed detection

Cited by 1 publication

References 7 publications

A generalization of the Jensen divergence: The chord gap divergence

A generalization of the Jensen divergence: The chord gap divergence

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