Introduces Off-log metric for correlation matrices and Grassmannian subspace distances to improve sensitivity and classification in fMRI brain network analysis across clinical and ageing datasets.
SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges, January 2026
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A Riemannian L-BFGS method with adapted Cauchy-point bound handling outperforms classical interior-point and L-BFGS-B solvers on mixed manifold-plus-bounds problems by orders of magnitude.
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Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces
Introduces Off-log metric for correlation matrices and Grassmannian subspace distances to improve sensitivity and classification in fMRI brain network analysis across clinical and ageing datasets.
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A Riemannian quasi-Newton algorithm for optimization with Euclidean bounds
A Riemannian L-BFGS method with adapted Cauchy-point bound handling outperforms classical interior-point and L-BFGS-B solvers on mixed manifold-plus-bounds problems by orders of magnitude.