From Covariance Matrices to Covariance Operators: Data Representation from Finite to Infinite-Dimensional Settings
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Publication:2954278
DOI10.1007/978-3-319-45026-1_5zbMath1353.62077OpenAlexW2528572894MaRDI QIDQ2954278
Ha Quang Minh, Vittorio Murino
Publication date: 12 January 2017
Published in: Algorithmic Advances in Riemannian Geometry and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-319-45026-1_5
Related Items (3)
Infinite-dimensional log-determinant divergences between positive definite Hilbert-Schmidt operators ⋮ Alpha-beta log-determinant divergences between positive definite trace class operators ⋮ 7 Manifold interpolation
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Cites Work
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- Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging
- Computing the Karcher mean of symmetric positive definite matrices
- Positive definite matrices
- Nonpositive curvature: A geometrical approach to Hilbert-Schmidt operators
- A Riemannian framework for tensor computing
- Affine-Invariant Riemannian Distance Between Infinite-Dimensional Covariance Operators
- Distances and inference for covariance operators
- The Geometric Mean, Matrices, Metrics, and More
- Information Geometry of Covariance Matrix: Cartan-Siegel Homogeneous Bounded Domains, Mostow/Berger Fibration and Fréchet Median
- On the Use of Matrix Information Geometry for Polarimetric SAR Image Classification
- Geometric Means in a Novel Vector Space Structure on Symmetric Positive‐Definite Matrices
- Metric Spaces and Positive Definite Functions
- Some properties of Gaussian reproducing kernel Hilbert spaces and their implications for function approximation and learning theory
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