[02429] Differential geometry with extreme eigenvalues in the positive semidefinite cone
Session Time & Room : 4E (Aug.24, 17:40-19:20) @G305
Type : Contributed Talk
Abstract : Geometric data in convex cones appear in a wide range of applications. Of particular interest is the space of symmetric positive definite (SPD) matrices and a variety of associated geometries that have been successfully exploited in medical imaging, neuroscience, and machine learning. In this talk, I will explore the Hilbert and Thompson geometries associated with SPD matrices and show that they offer a natural route to statistics based on extreme eigenvalues with promising computational properties.
