The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for
classification of electroencephalographic (EEG) recordings has been restricted by the lack of …

This paper proposes a new neural network based on SPD manifold learning for skeleton-
based hand gesture recognition. Given the stream of hand's joint positions, our approach …

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 …

By characterizing each image set as a nonsingular covariance matrix on the symmetric
positive definite (SPD) manifold, the approaches of visual content classification with image …

Despite the progress recently made towards automatic sleep staging for adults, children
have complicated sleep structures that require attention to the pediatric sleep staging. Semi …

Representing graphs as sets of node embeddings in certain curved Riemannian manifolds
has recently gained momentum in machine learning due to their desirable geometric …

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 …

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

The symmetric positive definite (SPD) matrices, forming a Riemannian manifold, are
commonly used as visual representations. The non-Euclidean geometry of the manifold …

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 …