We find an upper bound for geodesic distances associated to monotone Riemannian metrics on positive definite matrices and density matrices.
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Efficiently represents large geodesic distance matrices for MDS analysis.
problem Quadratic growth of geodesic distance matrices for large point sets.
method Sparse biharmonic interpolation to learn a subset of points for efficient approximation.
result 2x faster and 20x less memory usage than current methods, enabling analyses of large point sets.
Study of strictly accretive matrices using Finsler geometry.
problem Characterize the set of strictly accretive matrices.
method Introduced Finsler metrics and characterized geodesics and distance.
result Geodesic distance applied to matrix approximation problem.
Study of metrics on positive-definite matrices from power potential, linking to power means.
problem Understanding metrics on positive-definite matrices derived from power potential.
method Explicit expressions for geodesics and distance function derived from Hessian of power potential.
result Geodesics and distance function converge to weighted matrix geometric mean as β tends to zero.
This note improves correlation stress tests using geodesic distance.
problem Improving financial risk management through better covariance stress tests.
method Proposes a new geometrically invariant definition of correlation stress tests.
result Demonstrates a submanifold approach to stress testing covariance matrices.
This paper studies geodesics between covariance matrices of different ranks using the Bures-Wasserstein metric.
problem Geodesics between covariance matrices of varying ranks.
method Analyzes the Bures-Wasserstein distance on covariance matrices, completing previous work on geodesics and providing explicit formulas.
result The set of all minimizing geodesics between two covariance matrices is parametrized by a closed unit ball in .
Gradient descent algorithms on manifolds solve control and mean computation problems.
problem Control and mean computation on positive definite Hermitian matrices.
method Riemannian and natural gradient algorithms applied to geodesic distance.
result Efficient algorithms for control and mean computation demonstrated.
Totally geodesic submanifolds in the manifold SPD of symmetric positive-definite real matricesmath.DG
The paper explores totally geodesic submanifolds in SPD matrices and their properties.
problem Characterizing and understanding totally geodesic submanifolds in SPD matrices.
method Detailed geometric analysis and projection properties of SPD matrices.
result A non-linear projection on totally geodesic submanifolds has the minimizing property.
Bounds on geodesic distances on Stiefel manifold derived from new metrics.
problem Improving geodesic computation algorithms and understanding Stiefel manifold.
method New geometric insights and Lipschitz constants for geodesic distances.
result Explicit bounds on geodesic distances and conditions for attaining bounds.
Geometric analysis of normal distributions using Fisher and Killing metrics.
problem Quantifying the difference between Fisher and Killing metrics on the space of normal distributions.
method Riemannian geometry, Fisher information metric, Killing metric, asymptotic geodesics.
result Approximation of Fisher metric by Killing metric for long distances is justified.
An associated bundle approach to the Bures--Wasserstein geometry of fixed rank covariance matricesmath.DG
Paper explores geometry of covariance matrices using associated bundles.
problem Geometry of fixed-rank covariance matrices.
method Associated bundle approach to Bures--Wasserstein geometry.
result Established a one-to-one correspondence between geodesics.
In this paper we study the metric geometry of the space of positive invertible elements of a von Neumann algebra with a finite, normal and faithful tracial state . The trace induces an incomplete Riemannian metric , and though the techniques involved are quite different,…
Model tracks structural changes in Brownian particle configurations on a sphere.
problem Tracking structural changes in Brownian particle configurations on a sphere.
method Introduces Frustrated Distance Matrix (FDM) model for dynamic distance matrices on S^2.
result Preserves static BBS template with dynamics as redistributed spectral mass.
Log-Euclidean Lie Groupsmath.DG
Develops log-Euclidean Lie groups for SPD and correlation matrices.
problem Unifies various log-Euclidean constructions for SPD and correlation matrices.
method Theory and explicit isometries linking different log-Euclidean metrics.
result Explicit log-Euclidean metrics on SPD and correlation matrices.
Geometric structures and deviations on James' symmetric positive-definite matrix bicone domainstat.ML
New geometric structures defined on SPD matrices for better understanding.
problem Understanding SPD matrices and their geometric properties.
method Introducing Finslerian and dual information-geometric structures on James' bicone domain.
result Geodesics correspond to straight lines in coordinate systems, and new dissimilarities generalize existing ones.
New distances for comparing multivariate normal distributions.
problem Comparing multivariate normal distributions efficiently and accurately.
method Approximated Fisher-Rao distance and pullback SPD cone distances.
result Efficient computation of distances between normal distributions.
Researchers develop geodesics for a new metric on correlation matrices.
problem Lack of intrinsic tools for statistical analyses of correlation matrices.
method Developed geodesics for the quotient-affine metric on full-rank correlation matrices.
result Provided fundamental Riemannian operations for the quotient-affine metric.
This paper studies geometric properties of Wasserstein metric on SPD(n).
problem Understanding the geometry of symmetric positive-definite matrices under Wasserstein metric.
method Using fiber bundles, the paper derives explicit geometric quantities and proves global properties.
result The manifold is globally geodesic convex with non-negative curvatures but no conjugate pair and cut locus.
Two algorithms estimate Wasserstein distance matrices from few entries for manifold learning.
problem Estimating Wasserstein distance matrices from limited data for manifold learning.
method Proposes two algorithms: matrix completion and Nyström completion for square Wasserstein matrices.
result Nyström completion can outperform matrix completion with a fixed sample budget and improve classification stability.
We embed objects as elliptical distributions using the Wasserstein metric.
problem Embedding complex objects as vectors in low dimensional spaces.
method Embedding objects as elliptical probability distributions with the 2-Wasserstein metric.
result Wasserstein elliptical embeddings provide more intuitive and numerically stable tools than Gaussian embeddings.
geomstats offers efficient Riemannian geometry computations for machine learning.
problem Performing computations on manifolds in machine learning.
method Implementation of manifolds, metrics, geodesics, gradients, and loss functions.
result Efficient and user-friendly Riemannian geometry operations for machine learning.
Study the geometric properties of skew symmetric matrices and orthogonal groups.
problem Understanding the geometric properties of skew symmetric matrices and orthogonal groups.
method Investigate the differential-geometric properties of the exponential map and Riemannian structure.
result Connections between skew symmetric matrices and orthogonal groups are revealed.
Vanishing geodesic distances in infinite dimensions can be created.
problem Vanishing geodesic distances in infinite-dimensional spaces.
method Constructing a weak Riemannian metric in a Hilbert manifold.
result Vanishing geodesic distances can be engineered.
The paper connects geodesic nets to distance function critical points.
problem Understanding the relationship between geodesic nets and distance function critical points.
method Established a relationship between geodesic nets and critical points of the distance function.
result Bounded the number of balanced points and the length of certain minimizing geodesic nets.
Generates valid Euclidean distance matrices for molecular structures.
problem Generating point clouds in arbitrary rotations and translations is challenging.
method Developed a neural network architecture that produces valid Euclidean distance matrices invariant to rotations and translations.
result The architecture can generate molecular structures in a one-shot fashion by producing Euclidean distance matrices with a three-dimensional embedding.
Sub-Lorentzian geodesics on with the generating space of Hermitian matrices in the Lie algebra math.DG
Researchers found sub-Lorentzian geodesics on a specific Lie subgroup.
problem Finding geodesics on a specific Lie subgroup with a sub-Lorentzian metric.
method Formulated a time-anti-optimal control problem, applied Pontryagin's minimum principle, and used geodesics and shortest arcs of a sub-Riemannian metric.
result Discovered sub-Lorentzian nonspacelike geodesics and longest arcs.
The density matrices are positively semi-definite Hermitian matrices of unit trace that describe the state of a quantum system. The goal of the paper is to develop minimax lower bounds on error rates of estimation of low rank density matrices in trace regression models used in quantum state tomography (in particular, i…
Proposes a new Sliced-Wasserstein distance for covariance matrices in M/EEG signals.
problem Efficiently dealing with distributions of covariance matrices in M/EEG multivariate time series.
method Defines a Sliced-Wasserstein distance for symmetric positive definite matrices and applies it to brain-age prediction and Brain Computer Interface applications.
result Demonstrates computational efficiency and strong theoretical guarantees for the proposed distance.
Geodesic distance vanishes for certain Sobolev metrics on diffeomorphisms.
problem Analyzing geodesic distance in diffeomorphism groups for various Sobolev norms.
method Study of right-invariant Sobolev metrics on compactly supported diffeomorphisms.
result Geodesic distance vanishes identically for , and is positive otherwise.
New method for sampling orthogonal matrices using Hamiltonian Monte-Carlo.
problem Sampling from posterior distributions of orthogonal matrices in Bayesian models.
method Proposes a new sampling scheme based on Hamiltonian Monte-Carlo and Riemannian optimization.
result New method is comparable or faster in time per iteration and more sample-efficient than conventional methods.
This work proposes a model for geodesic distances and flows on manifolds.
problem Geodesic distances and flows on differentiable manifolds.
method Manifold-augmented Eikonal equation solutions.
result Geodesic flow provides globally length-minimizing curves.
New distance function proves rigidity in geodesic lamination space.
problem Proving rigidity in geodesic lamination space.
method Introduced left Hausdorff distance function and proved rigidity result.
result Extended mapping class group is isomorphic to bijections preserving left Hausdorff convergence.
Optimizes ground metric on graphs for evolving density models.
problem Optimizing ground metric for evolving density models.
method Adaptive ground metric learning constrained to geodesic distances on graphs.
result Efficiently learned geodesic distances align with observed density evolution.
The -metric or Fubini-Study metric on the non-linear Grassmannian of all submanifolds of type in a Riemannian manifold induces geodesic distance 0. We discuss another metric which involves the mean curvature and shows that its geodesic distance is a good topological metric. The vanishing phenomenon for…
This paper introduces a new method for SAR imagery region discrimination using geodesic distances.
problem Region discrimination in monopolarized SAR imagery.
method Geodesic distance between models.
result Advantages of using geodesic distance over stochastic distances.
Geodesic rays and chordal distances link algebraic and geometric properties of positive metrics.
problem Understanding the geometry of the space of positive metrics at infinity.
method Using Monge-Ampère equations and test configurations, algebraic descriptions of geodesic rays and chordal distances are derived.
result The Mabuchi chordal distance between geodesic rays associated with ample test configurations equals the spectral distance between their filtrations.
New method estimates geodesic distances using spherelets.
problem Accurately estimating geodesic distances on unknown manifolds.
method Uses spherelets to locally approximate unknown subspaces and estimate geodesic distances.
result Lower error for many manifolds, validated through simulations and real data.
URerF learns geodesic distances in noisy manifolds.
problem Learning geodesic distances in noisy high-dimensional data.
method Unsupervised random forest (URerF) with Bayesian Information Criterion.
result URerF outperforms other methods in estimating geodesic distances on noisy data.
Study geodesic distances and convexity in contact sets.
problem Understanding geodesic distances and convexity in contact sets.
method Extending results on quasi-psh functions and big cohomology classes, studying Monge-Ampère measures on contact sets.
result Convexity of the K-energy in big and nef cohomology classes.
Geometric framework for SPD matrices preserving subspace structures.
problem Processing SPD-valued data with preserved subspace structures.
method Thompson geometry of the semidefinite cone, extreme generalized eigenvalues, geodesic space structure.
result Novel inductive mean of SPD matrices based on Thompson geometry.
The distance function (or ) of a distance space (general metric space) is not differentiable in general. We investigate such distance spaces over , whose distance functions are differentiable like in case of Finsler spaces. These spaces have several good properties, yet they are no F…
Vanishing geodesic distance for the Riemannian metric with geodesic equation the KdV-equationmath.AP
The Virasoro-Bott group endowed with the right-invariant -metric (which is a weak Riemannian metric) has the KdV-equation as geodesic equation. We prove that this metric space has vanishing geodesic distance.
Circle's metric is at least π/4 away from any simply connected geodesic space.
problem Comparing simply connected geodesic spaces to the circle.
method Using Gromov-Hausdorff distance and topological properties.
result The Gromov-Hausdorff distance between circle and any simply connected geodesic space is at least π/4.
We introduce an universum of the Polish (=complete separable metric) space - the convex cone of distance matrices and study its geometry. It happened that the generic Polish spaces in this sense of this universum is so called Urysohn spaces defined by P.S.Urysohn in 20-th, and generic metric triple (= metric space with…
Positive definite matrices abound in a dazzling variety of applications. This ubiquity can be in part attributed to their rich geometric structure: positive definite matrices form a self-dual convex cone whose strict interior is a Riemannian manifold. The manifold view is endowed with a "natural" distance function whil…
Study geodesic curvature in Heisenberg group, interpreting it as distance correction.
problem Interpreting geodesic curvature in the Heisenberg group.
method Analyzing smooth horizontal curves in the Heisenberg group, interpreting curvature as distance correction.
result Geodesic curvature in Heisenberg group is the first term in distance expansion.
Assigns compact set distance-like functions to non-compact geodesic spaces.
problem Assigning distance-like functions to compact sets in non-compact geodesic spaces.
method Assigns each compact set a distance-like function and studies the pseudo-metric on the space of compact subsets.
result Obtains a pseudo-metric on the space of compact subsets that is less than the Hausdorff distance.
A method for learning embeddings from multi-view data using Gromov-Wasserstein.
problem Challenges in learning low-dimensional representations from multi-view relational data with differing geometries.
method Bary-GWMDS and Mean-GWMDS-C, Gromov-Wasserstein-based methods operating on distance matrices.
result Stable and geometrically meaningful embeddings learned from synthetic and real-world datasets.