arXiv Machine Learning
Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs
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Sparse Covariance Neural Networks
arXiv:2410. 01669v3 Announce Type: replace Abstract: Covariance Neural Networks (VNNs) perform graph convolutions on the covariance matrix of input data to leverage correlation information as pairwise connections.
arXiv AI
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SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges
This review discusses how neuroimaging data can be represented as symmetric positive-definite (SPD) matrices and analyzed using the Riemannian geometry of the SPD manifold. It surveys the evolution from modality-specific SPD representations to geometric shallow and deep learning methods, emphasizing how these approaches maintain structural constraints while integrating modern AI techniques. The paper frames SPD matrix learning as a bridge between classical geometric statistics and contemporary machine learning in neuroimaging and brain‑computer interface research.
arXiv Machine Learning
Aug 24
Interpretable Information-Decomposed Brain Graph Learning for fMRI-based Disease Diagnosis
arXiv:2608.20380v1 Announce Type: cross Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) has enabled non-invasive mapping of functional brain interactions for computer-aided di...
arXiv Machine Learning
Jul 15
Graph Regularized PCA
arXiv:2601. 10199v2 Announce Type: replace Abstract: Multivariate data often exhibit complex dependencies that violate the assumption of isotropic residual noise.
arXiv Machine Learning
Jun 16
Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs
arXiv:2602. 10031v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral GNNs, reflecting two largely separate research traditions in machine learning and signal processing.
arXiv Machine Learning
Aug 18
BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces
arXiv:2608. 15266v1 Announce Type: cross Abstract: Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer's disease.