AI RESEARCH
Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces
arXiv CS.LG
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ArXi:2605.22334v1 Announce Type: new Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the curved geometry of correlation space. Existing geometric methods frequently lack closed-form operations or depend on arbitrary region ordering, limiting scalability. We