Clustering with Bregman Divergences
The Journal of Machine Learning Research
Fitting the smallest enclosing bregman ball
ECML'05 Proceedings of the 16th European conference on Machine Learning
Sparse Multiscale Patches for Image Processing
Emerging Trends in Visual Computing
Sided and symmetrized Bregman centroids
IEEE Transactions on Information Theory
Klee sets and Chebyshev centers for the right Bregman distance
Journal of Approximation Theory
Channel capacity restoration of noisy optical quantum channels
NEHIPISIC'11 Proceeding of 10th WSEAS international conference on electronics, hardware, wireless and optical communications, and 10th WSEAS international conference on signal processing, robotics and automation, and 3rd WSEAS international conference on nanotechnology, and 2nd WSEAS international conference on Plasma-fusion-nuclear physics
Approximate bregman near neighbors in sublinear time: beyond the triangle inequality
Proceedings of the twenty-eighth annual symposium on Computational geometry
Pattern learning and recognition on statistical manifolds: an information-geometric review
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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We present a generalization of Welzl's smallest enclosing disk algorithm [E. Welzl, Smallest enclosing disks (balls and ellipsoids), in: New Results and New Trends in Computer Science, in: Lecture Notes in Computer Science, vol. 555, Springer, 1991, pp. 359-370] for point sets lying in information-geometric spaces. Given a set of points equipped with a Bregman divergence as a (dis)similarity measure, we investigate the problem of finding its unique (circum)center defined as the point minimizing the maximum divergence to the point set. As an application, we show how to solve a statistical model estimation problem by computing the center of a finite set of univariate normal distributions.