  We present a geometric framework for the processing of SPD-valued data that
preserves subspace structures and is based on the efficient computation of
extreme generalized eigenvalues. This is achieved through the use of the
Thompson geometry of the semidefinite cone. We explore a particular geodesic
space structure in detail and establish several properties associated with it.
Finally, we review a novel inductive mean of SPD matrices based on this
geometry.
