4 citations · 14 across the 16 of their papers we have counts for
17 papers
A Sliced-Wasserstein Framework on Correlation Matrices for EEG Decoding
Chen Hu, Rui Wang, Jiale Zhou +4
Electroencephalography (EEG) offers noninvasive, millisecond resolution recordings of neuronal activity and is widely used in neuroscience and healthcare. Many EEG decoding pipelin…
Wasserstein-Aligned Hyperbolic Multi-View Clustering
Rui Wang, Yuting Jiang, Xiaoqing Luo +3
Multi-view clustering (MVC) aims to uncover the latent structure of multi-view data by learning view-common and view-specific information. Although recent studies have explored hyp…
Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometry
Rui Wang, Shaocheng Jin, Ziheng Chen +2
Covariance matrices have proven highly effective across many scientific fields. Since these matrices lie within the Symmetric Positive Definite (SPD) manifold - a Riemannian space…
SMLNet: A SPD Manifold Learning Network for Infrared and Visible Image Fusion
Huan Kang, Hui Li, Tianyang Xu +4
Euclidean representation learning methods have achieved promising results in image fusion tasks, which can be attributed to their clear advantages in handling with linear space. Ho…
Riemannian Self-Attention Mechanism for SPD Networks
Rui Wang, Xiao-Jun Wu, Hui Li +1
Symmetric positive definite (SPD) matrix has been demonstrated to be an effective feature descriptor in many scientific areas, as it can encode spatiotemporal statistics of the dat…
PLPCA: Persistent Laplacian Enhanced-PCA for Microarray Data Analysis
Sean Cottrell, Rui Wang, Guowei Wei
Over the years, Principal Component Analysis (PCA) has served as the baseline approach for dimensionality reduction in gene expression data analysis. It primary objective is to ide…