With the rapid advances in digital imaging and communication technologies, recently image
set classification has attracted significant attention and has been widely used in many real …

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 …

It is a challenge for bearing fault diagnosis to effectively reduce the dimension of high
dimensional data and improve the accuracy of fault identification. To address this issue, a …

Vision Transformers are very popular nowadays due to their state-of-the-art performance in
several computer vision tasks, such as image classification and action recognition. Although …

The precise estimation of the state of health (SoH) in Lithium-ion batteries (LiBs) relies
heavily on a reliable health indicator (HI). Conventional indicators are often constructed by …

In the community of artificial intelligence, significant progress has been made in encoding
sequential data using deep learning techniques. Nevertheless, how to effectively mine …

Extracting features from EEG signals through time, frequency and spatial-domain gives rise
to the problem of neglecting the property of nonlinear manifold structure of the data, and …

Because the distinct advantages of manifold-learning methods for feature extraction,
Riemannian manifolds have been used extensively in image recognition tasks in recent …

Determining the low-energy eigenspectra of quantum many-body systems is a long-standing
challenge in physics. In this paper, we solve this problem by introducing two algorithms to …

Representing image sets as subspaces on Grassmann manifold and leveraging the
Riemannian geometry of this space has proven to be highly effective in various visual …