With the rapid development of next‐generation wireless communication technologies and
the increasing demand of spectrum resources, it becomes necessary to introduce learning …

We present a new Riemannian metric, termed Log-Cholesky metric, on the manifold of
symmetric positive definite (SPD) matrices via Cholesky decomposition. We first construct a …

It is well known that the application of radar is becoming more and more popular with the
development of the signal technology progress. This paper lists the current radar signal …

This paper presents one feature-based detector to find sea-surface floating small targets. In
integration time of the order of seconds, target returns exhibit time-frequency (TF) …

This article deals with the problem of detecting maritime targets embedded in
nonhomogeneous sea clutter, where the limited number of secondary data is available due …

Principal component analysis (PCA) is a commonly used pattern analysis method that maps
high-dimensional data into a lower-dimensional space maximizing the data variance, that …

Data, which lie in the space P m, of m× m symmetric positive definite matrices,(sometimes
called tensor data), play a fundamental role in applications, including medical imaging …

We develop geometric optimization on the manifold of Hermitian positive definite (HPD)
matrices. In particular, we consider optimizing two types of cost functions:(i) geodesically …

Hermitian positive definite (hpd) matrices form a self-dual convex cone whose interior is a
Riemannian manifold of nonpositive curvature. The manifold view comes with a natural …

We study first-order optimization algorithms for computing the barycenter of Gaussian
distributions with respect to the optimal transport metric. Although the objective is …