Clustering with Bregman Divergences
The Journal of Machine Learning Research
On the smallest enclosing information disk
Information Processing Letters
Bregman distances and Klee sets
Journal of Approximation Theory
Bregman distances and Chebyshev sets
Journal of Approximation Theory
Fitting the smallest enclosing bregman ball
ECML'05 Proceedings of the 16th European conference on Machine Learning
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We systematically investigate the farthest distance function, farthest points, Klee sets, and Chebyshev centers, with respect to Bregman distances induced by Legendre functions. These objects are of considerable interest in Information Geometry and Machine Learning; when the Legendre function is specialized to the energy, one obtains classical notions from Approximation Theory and Convex Analysis. The contribution of this paper is twofold. First, we provide an affirmative answer to a recently-posed question on whether or not every Klee set with respect to the right Bregman distance is a singleton. Second, we prove uniqueness of the Chebyshev center and we present a characterization that relates to previous works by Garkavi, by Klee, and by Nielsen and Nock.