By representing each image set as a nonsingular covariance matrix on the symmetric
positive definite (SPD) manifold, visual classification with image sets has attracted much …

In this paper, we comparatively analyze the Bures-Wasserstein (BW) geometry with the
popular Affine-Invariant (AI) geometry for Riemannian optimization on the symmetric positive …

Performing pattern analysis over the symmetric positive definite (SPD) manifold requires
specific mathematical computations, characterizing the non-Euclidian property of the …

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

The automatic seizure detection in electroencephalogram (EEG) signals is crucial for the
monitoring, diagnosis, and treatment of epilepsy. In this study, an intelligent detection …

Different from traditional action recognition based on video segments, online action
recognition aims to recognize actions from an unsegmented stream of data in a continuous …

Symmetric positive definite (SPD) matrices have been employed for data representation in
many visual recognition tasks. The success is mainly attributed to learning discriminative …

Non-Euclidean data is increasingly used in practical applications. As a typical
representative, Symmetric Positive Definite (SPD) matrices can form a Riemannian manifold …

We study metric properties of symmetric divergences on Hermitian positive definite matrices.
In particular, we prove that the square root of these divergences is a distance metric. As a …

In kernel methods, the kernels are often required to be positive definitethat restricts the use
of many indefinite kernels. To consider those nonpositive definite kernels, in this paper, we …