We present a simple method to approximate the Fisher–Rao distance between multivariate
normal distributions based on discretizing curves joining normal distributions and …

We introduce Geomstats, an open-source Python package for computations and statistics on
nonlinear manifolds such as hyperbolic spaces, spaces of symmetric positive definite …

In this work, a framework for optimizing the configuration of service areas in airspace into
disparate partitions is demonstrated in the context of urban air mobility (UAM) operations …

Vector Quantization is the name given to discretization methods based on nearest
neighbour search. It was developed in the 1950s, mostly in signal processing and …

Conventional subspace clustering algorithms group the data samples by optimizing the
objective function which aggregates different clustering criteria using the linear combination …

The Fisher information metric provides a smooth family of probability measures with a
Riemannian manifold structure, which is an object in information geometry. The information …

Since the popularization of the Stiefel manifold for numerical applications in 1998 in a
seminal paper from Edelman et al., it has been exhibited to be a key to solve many problems …

The quantization problem looks for best approximations of a probability measure on a given
metric space by finitely many points, where the approximation error is measured with respect …

We investigate the minimal error in approximating a general probability measure $\mu $ on
$\mathbb {R}^ d $ by the uniform measure on a finite set with prescribed cardinality $ n …

In this paper, the similarity measurement issue, in the context of texture images comparison,
is tackled from a geometrical point of view by computing the Rao Geodesic distance on the …