Monday, October 28, 2013 - 2:00pm - 2:50pm
Jun Zhang (University of Michigan)
Divergence functions, as a proximity measure on a smooth manifold and often surrogate to the (symmetric) metric function, play an important role in machine learning, statistical inference, optimization, etc. This talk will review the various geometric structures induced from a divergence function defined on a manifold. Most importantly, a Riemannian metric with a pair of torsion-free affine connections can be induced on the manifold, the so-called the “statistical structure” in Information Geometry.
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