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Computer Science > Computer Vision and Pattern Recognition

arXiv:1009.4004 (cs)
[Submitted on 21 Sep 2010 (v1), last revised 19 Dec 2011 (this version, v2)]

Title:A family of statistical symmetric divergences based on Jensen's inequality

Authors:Frank Nielsen
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Abstract:We introduce a novel parametric family of symmetric information-theoretic distances based on Jensen's inequality for a convex functional generator. In particular, this family unifies the celebrated Jeffreys divergence with the Jensen-Shannon divergence when the Shannon entropy generator is chosen. We then design a generic algorithm to compute the unique centroid defined as the minimum average divergence. This yields a smooth family of centroids linking the Jeffreys to the Jensen-Shannon centroid. Finally, we report on our experimental results.
Comments: 15 pages, 2 figure
Subjects: Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT)
Cite as: arXiv:1009.4004 [cs.CV]
  (or arXiv:1009.4004v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.1009.4004

Submission history

From: Frank Nielsen [view email]
[v1] Tue, 21 Sep 2010 06:32:52 UTC (63 KB)
[v2] Mon, 19 Dec 2011 02:31:16 UTC (54 KB)
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