A Riemannian structure for correlation matrices
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Publication:5205405
DOI10.7153/oam-2019-13-46zbMath1433.62149OpenAlexW2974392686WikidataQ115157715 ScholiaQ115157715MaRDI QIDQ5205405
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Publication date: 11 December 2019
Published in: Operators and Matrices (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.7153/oam-2019-13-46
Statistics on manifolds (62R30) Measures of association (correlation, canonical correlation, etc.) (62H20) Numerical computation of eigenvalues and eigenvectors of matrices (65F15)
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Cites Work
- Statistics on the manifold of multivariate normal distributions: theory and application to diffusion tensor MRI processing
- Diagonality measures of Hermitian positive-definite matrices with application to the approximate joint diagonalization problem
- The Riemannian geometry of the space of positive-definite matrices and its application to the regularization of positive-definite matrix-valued data
- Visualization and processing of tensor fields.
- A Riemannian framework for tensor computing
- Canonical Correlation Analysis on SPD(n) Manifolds
- Optimization Methods on Riemannian Manifolds and Their Application to Shape Space
- The Geometry of Algorithms with Orthogonality Constraints
- A new, globally convergent Riemannian conjugate gradient method
- Riemannian Gaussian Distributions on the Space of Symmetric Positive Definite Matrices
- A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite Matrices
- Geometric Means in a Novel Vector Space Structure on Symmetric Positive‐Definite Matrices