Product Geometries on Cholesky Manifolds with Applications to SPD Manifolds
- URL: http://arxiv.org/abs/2407.02607v1
- Date: Tue, 2 Jul 2024 18:46:13 GMT
- Title: Product Geometries on Cholesky Manifolds with Applications to SPD Manifolds
- Authors: Ziheng Chen, Yue Song, Xiao-Jun Wu, Nicu Sebe,
- Abstract summary: We present two new metrics on the Symmetric Positive Definite (SPD) manifold via the Cholesky manifold.
Our metrics are easy to use, computationally efficient, and numerically stable.
- Score: 65.04845593770727
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This paper presents two new metrics on the Symmetric Positive Definite (SPD) manifold via the Cholesky manifold, i.e., the space of lower triangular matrices with positive diagonal elements. We first unveil that the existing popular Riemannian metric on the Cholesky manifold can be generally characterized as the product metric of a Euclidean metric and a Riemannian metric on the space of n-dimensional positive vectors. Based on this analysis, we propose two novel metrics on the Cholesky manifolds, i.e., Diagonal Power Euclidean Metric and Diagonal Generalized Bures-Wasserstein Metric, which are numerically stabler than the existing Cholesky metric. We also discuss the gyro structures and deformed metrics associated with our metrics. The gyro structures connect the linear and geometric properties, while the deformed metrics interpolate between our proposed metrics and the existing metric. Further, by Cholesky decomposition, the proposed deformed metrics and gyro structures are pulled back to SPD manifolds. Compared with existing Riemannian metrics on SPD manifolds, our metrics are easy to use, computationally efficient, and numerically stable.
Related papers
- RMLR: Extending Multinomial Logistic Regression into General Geometries [64.16104856124029]
Our framework only requires minimal geometric properties, thus exhibiting broad applicability.
We develop five families of SPD MLRs under five types of power-deformed metrics.
On rotation matrices we propose Lie MLR based on the popular bi-invariant metric.
arXiv Detail & Related papers (2024-09-28T18:38:21Z) - The Fisher-Rao geometry of CES distributions [50.50897590847961]
The Fisher-Rao information geometry allows for leveraging tools from differential geometry.
We will present some practical uses of these geometric tools in the framework of elliptical distributions.
arXiv Detail & Related papers (2023-10-02T09:23:32Z) - Adaptive Log-Euclidean Metrics for SPD Matrix Learning [73.12655932115881]
We propose Adaptive Log-Euclidean Metrics (ALEMs), which extend the widely used Log-Euclidean Metric (LEM)
The experimental and theoretical results demonstrate the merit of the proposed metrics in improving the performance of SPD neural networks.
arXiv Detail & Related papers (2023-03-26T18:31:52Z) - A singular Riemannian geometry approach to Deep Neural Networks I.
Theoretical foundations [77.86290991564829]
Deep Neural Networks are widely used for solving complex problems in several scientific areas, such as speech recognition, machine translation, image analysis.
We study a particular sequence of maps between manifold, with the last manifold of the sequence equipped with a Riemannian metric.
We investigate the theoretical properties of the maps of such sequence, eventually we focus on the case of maps between implementing neural networks of practical interest.
arXiv Detail & Related papers (2021-12-17T11:43:30Z) - On Riemannian Optimization over Positive Definite Matrices with the
Bures-Wasserstein Geometry [45.1944007785671]
We comparatively analyze the Bures-Wasserstein (BW) geometry with the popular Affine-Invariant (AI) geometry.
We build on an observation that the BW metric has a linear dependence on SPD matrices in contrast to the quadratic dependence of the AI metric.
We show that the BW geometry has a non-negative curvature, which further improves convergence rates of algorithms over the non-positively curved AI geometry.
arXiv Detail & Related papers (2021-06-01T07:39:19Z) - A Unifying and Canonical Description of Measure-Preserving Diffusions [60.59592461429012]
A complete recipe of measure-preserving diffusions in Euclidean space was recently derived unifying several MCMC algorithms into a single framework.
We develop a geometric theory that improves and generalises this construction to any manifold.
arXiv Detail & Related papers (2021-05-06T17:36:55Z) - Curvatures of Stiefel manifolds with deformation metrics [0.0]
We compute curvatures of a family of tractable metrics on Stiefel manifold.
The metrics could be identified with the Cheeger deformation metrics.
arXiv Detail & Related papers (2021-05-05T02:13:38Z) - Operator-valued formulas for Riemannian Gradient and Hessian and
families of tractable metrics [0.0]
We provide a formula for a quotient of a manifold embedded in an inner product space with a non-constant metric function.
We extend the list of potential metrics that could be used in manifold optimization and machine learning.
arXiv Detail & Related papers (2020-09-21T20:15:57Z) - Spectral Flow on the Manifold of SPD Matrices for Multimodal Data
Processing [17.162497914078322]
We consider data acquired by multimodal sensors capturing complementary aspects and features of a measured phenomenon.
We focus on a scenario in which the measurements share mutual sources of variability but might also be contaminated by other measurement-specific sources.
arXiv Detail & Related papers (2020-09-17T04:38:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.