Fitting the Smallest Enclosing Bregman Ball

Abstract

Abstract. Finding a point which minimizes the maximal distortion with respect to a dataset is an important estimation problem that has recently received growing attentions in machine learning, with the advent of one class classification. We propose two theoretically founded generalizations to arbitrary Bregman divergences, of a recent popular smallest enclosing ball approximation algorithm for Euclidean spaces coined by Bădoiu and Clarkson in 2002.

Download PDF:
_{Sorry, we are unable to provide the full text but you may find it at the following location(s):}

http://citeseerx.ist.psu.edu/v... (external link)

http://www.lix.polytechnique.f... (external link)