Workshop
Information geometry and its applications to learning
- Shun-ichi Amari (RIKEN, Japan)
Abstract
Information geometry studies the invariant geometrical structure of a manifold of probability distributions. It consists of Riemannian metric due to Fisher information and a pair of dually coupled affine connections. Since learning takes place under stochastic environments, it provides a useful new tool to various aspects of learning. The present talk begins with an intuitive introduction to information geometry, where prior knowledge on differential geometry is not required. After introducing nice properties such as the generalized Pythagorean theorem, various applications to machine learning and statistical inference are demonstrated.
