Geometric data summarization has become an essential tool in both geometric
approximation algorithms and where geometry intersects with big data problems. In linear or …

We develop a new geometric approach for high resolution Doppler processing based on the
Riemannian geometry of Toeplitz covariance matrices and the notion of Riemannian p …

We study the problem of finding the global Riemannian center of mass of a set of data points
on a Riemannian manifold. Specifically, we investigate the convergence of constant step …

We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by
considering a variational definition with respect to a generic mean, thereby extending the …

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

Set-membership estimation (SME) outputs a set estimator that guarantees to cover the
groundtruth. Such sets are, however, defined by (many) abstract (and potentially nonconvex) …

Theoretical background is provided towards the mathematical foundation of the minimum
enclosing ball problem. This problem concerns the determination of the unique spherical …

Clustering categorical distributions in the probability simplex is a fundamental task met in
many applications dealing with normalized histograms. Traditionally, differential-geometric …

Interior-point methods offer a highly versatile framework for convex optimization that is
effective in theory and practice. A key notion in their theory is that of a self-concordant …

Data sets of multivariate normal distributions abound in many scientific areas like diffusion
tensor medical imaging, structure tensor computer vision, radar signal processing, machine …