Covariance matrices have attracted attention for machine learning applications due to their
capacity to capture interesting structure in the data. The main challenge is that one needs to …

Geometric deep learning is a relatively nascent field that has attracted significant attention in
the past few years. This is partly due to the availability of data acquired from non-euclidean …

Functional data analysis (FDA) is a fast-growing area of research and development in
statistics. While most FDA literature imposes the classical L 2 Hilbert structure on function …

Automated anatomical labeling plays a vital role in coronary artery disease diagnosing
procedure. The main challenge in this problem is the large individual variability inherited in …

Neural network on Riemannian symmetric space such as hyperbolic space and the manifold
of symmetric positive definite (SPD) matrices is an emerging subject of research in …

Hand gesture recognition (HGR) plays a significant role in interpreting the meaning of sign
language, human–computer interaction, and robot control. This paper proposes a real-time …

Matrix manifolds, such as manifolds of Symmetric Positive Definite (SPD) matrices and
Grassmann manifolds, appear in many applications. Recently, by applying the theory of …

We propose the use of the vector-valued distance to compute distances and extract
geometric information from the manifold of symmetric positive definite matrices (SPD), and …

Recent research has shown that alignment between the structure of graph data and the
geometry of an embedding space is crucial for learning high-quality representations of the …

Abstract Representation learning with Symmetric Positive Semi-definite (SPSD) matrices
has proven effective in many machine learning problems. Recently, some SPSD neural …