As data is a predominant resource in applications, Riemannian geometry is a natural
framework to model and unify complex nonlinear sources of data. However, the …

In this paper, we propose a novel method for representation and classification of two-person
interactions from 3D skeleton sequences. The key idea of our approach is to use Gaussian …

We develop an advanced approach for extending Gaussian Differential Privacy (GDP) to
general Riemannian manifolds. The concept of GDP stands out as a prominent privacy …

The epileptic electroencephalography (EEG) classification technique has been extensively
adopted in epilepsy diagnosis and management due to its powerful capacity in …

A Stiefel manifold of the compact type is often encountered in many fields of engineering
including, signal and image processing, machine learning, numerical optimization and …

Understanding sign gestures is an essential step to helping individuals with hearing
impaired. The existing works can only identify a small set of gestures accurately and the …

Low resolution, difficult illumination and noise are the important factors that affect the
performance of face recognition system. In order to counteract these adverse factors, in this …

Background. Lattice theory extends into virtually every branch of mathematics, ranging from
measure theory and convex geometry to probability theory and topology. A more recent …

Objective: We present a transfer learning method for datasets with different dimensionalities,
coming from different experimental setups but representing the same physical phenomena …

The eigenvalue probability density function (PDF) for the Gaussian unitary ensemble has a
well-known analogy with the Boltzmann factor for a classical log-gas with pair potential− log …