The paper embeds manifolds into finite Euclidean spaces using eigenvector fields of the connection Laplacian.
problem Embedding manifolds into finite-dimensional Euclidean spaces using eigenvector fields of the connection Laplacian.
method Constructing local coordinate charts with low distortion using eigenvector fields and proving estimates for eigenvector fields and the heat kernel.
result The distortion constants depend only on geometric properties of manifolds in the little Hölder space c2,α, allowing for embedding into a finite-dimensional Euclidean space. New metric tensor field on symmetric matrices simplifies eigenvector computation.
problem Complex eigenvector computation for 2x2 symmetric matrices.
method Introducing a metric tensor field on the space of symmetric matrices, resulting in a curved manifold.
result Parallel transport simplifies eigenvector computation for one-parameter families of matrices.
New neural architectures invariant to sign flips and basis symmetries for graph representation learning.
problem Learning invariant graph representations from eigenvectors.
method SignNet and BasisNet neural architectures that are invariant to sign flips and basis symmetries.
result Proven to be universal, approximating any continuous function of eigenvectors with desired invariances.
Study of quantum Riemannian geometries over binary field, finding many non-flat examples.
problem Classifying parallelizable quantum Riemannian geometries over F2. method Classification of geometries up to dimension 3, characterizing quantum Laplacian eigenvectors.
result Found many non-flat quantum Riemannian geometries over F2. New method improves subspace iteration for eigenvectors in machine learning.
problem Computing eigenvectors for large-scale problems in machine learning.
method Subspace iteration with ℓ2o∞ norm convergence analysis. result Deterministic bounds and practical stopping criterion for improved performance.
Describes maps with prescribed eigenvectors of Jacobian matrices.
problem Maps with specific eigenvectors of Jacobian matrices.
method Coordinate-independent definition of Jacobian, Frobenius integrability theorems, rich partial frames.
result Complete analysis for rich partial frames, partial results for non-rich and non-involutive cases.
We introduce and study H-paracontact metric manifolds, that is, paracontact metric manifolds whose Reeb vector field ξ is harmonic. We prove that they are characterized by the condition that ξ is a Ricci eigenvector. We then investigate how harmonicity of the Reeb vector field ξ of a paracontact metric manifold…
We study the problem of constructing systems of hyperbolic conservation laws in one space dimension with prescribed eigencurves, i.e. the eigenvector fields of the Jacobian of the flux are given. We formulate this as a typically overdetermined system of equations for the eigenvalues-to-be. Equivalent formulations in te…
Study space-like surfaces in Robertson-Walker spacetimes with specific geometric conditions.
problem Characterize space-like surfaces in Robertson-Walker spacetimes with given geometric conditions.
method Investigate surfaces satisfying specific conditions on tangential and normal parts of the unit vector field, using shape operators and minimal surfaces.
result Classification theorem and parametrizations of space-like class A surfaces in L14(f,0). A new algorithm improves Wasserstein discriminant analysis for better data classification.
problem Improving data classification in machine learning.
method Bi-level nonlinear eigenvector algorithm (WDA-nepv) for optimal transport and trace ratio optimizations.
result WDA-nepv enhances classification accuracy and scalability.
Random matrix analysis reveals that neural network weights are mostly random, with some indicating learned information.
problem Understanding how neural networks store information needed for tasks.
method Random matrix theory (RMT) applied to weight matrices of trained deep neural networks.
result Most singular values and eigenvectors of trained neural networks follow universal RMT predictions, suggesting they are random and do not contain system-specific information.
Study eigenvector overlaps in large Gaussian matrices, simplifying for GOE.
problem Investigate eigenvector overlaps in large Gaussian matrices.
method Analysis of eigenvector flow under Dyson Brownian motion.
result Explicit computation of limiting rescaled mean squared overlaps.
Study shows how many samples are needed for eigenvector/eigenvalue accuracy.
problem Guaranteeing eigenvector and eigenvalue accuracy of sample vs actual covariance matrices.
method Proves inner product decrease proportional to eigenvalue distance for various distributions.
result Non-asymptotic concentration bounds and conditions for distinguishing principal components.
Machine learning models perform better with location coordinates alone, not Moran Eigenvectors.
problem Improving machine learning models for spatial data.
method Examined Moran Eigenvectors as additional spatial features in machine learning models using synthetic datasets.
result Machine learning models using only location coordinates achieve better accuracies than eigenvector-based approaches.
Paper addresses eigenvector perturbation in small eigen-gap scenarios.
problem Fine-grained behavior of eigenvectors in the presence of small eigen-gaps.
method Develops de-biased estimators for linear functions of an unknown eigenvector.
result Achieves minimax lower bounds for a family of scenarios, even with small eigen-gaps.
In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks "nearby" that prespecified target region. For example, one might be interested in the clusteri…
We calculate eigenvector overlaps between intersecting time periods of covariance matrices.
problem Analyzing overlapping time periods in covariance matrices.
method Girko linearisation and extended local laws.
result Computed eigenvector overlaps for intersecting time intervals.
New methods find eigenvectors faster than Lanczos's method.
problem Finding the leading eigenvector efficiently.
method Coordinate-wise methods combining shift-and-invert with linear regression.
result Global convergence with runtime guarantees better than Lanczos's method.
In spectral clustering, one defines a similarity matrix for a collection of data points, transforms the matrix to get the Laplacian matrix, finds the eigenvectors of the Laplacian matrix, and obtains a partition of the data using the leading eigenvectors. The last step is sometimes referred to as rounding, where one ne…
New method estimates sparse eigenvectors without sacrificing orthogonality.
problem Estimating sparse eigenvectors of a symmetric matrix.
method Developed a new method using MM framework and Procrustes reformulation.
result Improves support recovery and explained variance compared to existing methods.
Spectral clustering performance depends on eigenvector fluctuations, shown to be Gaussian.
problem Predicting the performance of spectral clustering.
method General spike random matrix model and rotational invariance of noise.
result Fluctuations of eigenvector entries are Gaussian in large-dimensional regime.
New algorithm updates eigenvectors of evolving graphs efficiently.
problem Updating eigenvectors of dynamic graphs.
method Subspace projection based on Rayleigh-Ritz projections.
result Strong performance in eigenvector approximation and downstream tasks.
New algorithm consistently orients eigenvectors for machine learning.
problem Inconsistent eigenvector orientation in machine learning.
method Postprocesses well-established eigen calls to create consistently oriented eigenvectors.
result Interpretable time series of training weights in machine learning models.
The study proves a central limit theorem for eigenvectors of the normalized Laplacian in random graphs.
problem Understanding the distribution of eigenvectors of the normalized Laplacian in random graphs.
method Proving a central limit theorem for eigenvectors of the normalized Laplacian for random graphs.
result The components of the eigenvectors corresponding to the largest eigenvalues of the normalized Laplacian matrix converge to multivariate normals.
A new algorithm reduces online eigenvector computation time while maintaining optimal performance.
problem Online learning of top eigenvectors in both adversarial and stochastic settings.
method Follow the Compressed Leader (FTCL) framework, compressing the matrix strategy to dimensions 3 (adversarial) and 1 (stochastic).
result Achieves optimal regret without sacrificing running time, resolving open questions.
New theory for eigenvectors of generalized Laplacian matrices, addressing dependency issues.
problem Dependency in random matrix theory hinders eigenvector analysis for latent embeddings.
method Introduces generalized Laplacian matrices and a new asymptotic theory framework.
result Established asymptotic normalities for spiked eigenvectors and eigenvalues.
Fast algorithm recovers principal eigenvector from noisy matrices.
problem Recovering the first principal eigenvector from noisy positive semidefinite matrices.
method Cone projected power iteration algorithm.
result Achieves polynomial time complexity and small error for certain convex cones.
A simple property of Weyl tensor in shear-free, vorticity-free, acceleration-free velocity fields.
problem Proving a property of the Weyl tensor in specific velocity fields.
method Analyzing the Weyl tensor's divergence and contraction properties in shear-free, vorticity-free, acceleration-free velocity fields.
result The covariant divergence of the Weyl tensor is zero if the contraction of the Weyl tensor with the velocity is zero, and vice versa.
Proves a Gel'fand-Kolmogoroff type result for vector bundles and polynomial functions.
problem Characterizing vector bundles and polynomial functions using differential operators.
method Analyzes the associative structure of symbols of differential operators and their eigenvectors.
result Derives a Gel'fand-Kolmogoroff type result for the algebra of symbols of differential operators.
The paper explores how kernel eigenalignments affect generalization in KRR.
problem Achieving robust generalization in kernel methods.
method Direct connection between generalization and matrix eigenvectors/eigenvalues, focusing on finite-sample settings.
result Strong generalization requires increasing eigenvector alignment, eigenvalue magnitude, or gaps between eigenvalues.
This paper develops the exact linear relationship between the leading eigenvector of the unnormalized modularity matrix and the eigenvectors of the adjacency matrix. We propose a method for approximating the leading eigenvector of the modularity matrix, and we derive the error of the approximation. There is also a comp…
A new method approximates Laplacian eigenvectors for RL efficiently.
problem Efficiently learning state representations in RL.
method General and scalable approach to approximating Laplacian eigenvectors.
result Empirically shows improved performance in RL tasks.
Paper tackles small eigen-gap estimation and inference for noisy symmetric matrices.
problem Estimating eigenvectors with small eigen-gap and fine-grained statistical reasoning.
method Eigen-decomposition of asymmetric data matrix, distribution-free procedures, adaptive to heteroscedastic noise.
result Minimax optimal under Gaussian noise, confidence intervals for eigenvalues, small eigen-gap handling.
Improved spectral clustering with fewer eigenvectors performs better.
problem Improving spectral clustering performance under weaker conditions.
method Tighter analysis and using fewer eigenvectors for embedding.
result Spectral clustering can produce better results with fewer eigenvectors.
New insights into spectral clustering reveal strong connections within eigenvectors.
problem Clustering on graphs when there are two underlying clusters.
method Analyzes the eigenvector corresponding to the second largest eigenvalue of the adjacency matrix.
result Vertices with extreme values in the eigenvector are more reliably classified.
The original contributions of this paper are twofold: a new understanding of the influence of noise on the eigenvectors of the graph Laplacian of a set of image patches, and an algorithm to estimate a denoised set of patches from a noisy image. The algorithm relies on the following two observations: (1) the low-index e…
This paper proves the convergence rate of Krasulina's estimator for least eigenvalue and eigenvector.
problem Finding the least eigenvalue and eigenvector of an unknown covariance matrix.
method Developed a convergence proof for Krasulina's estimator.
result Established the convergence rate of Krasulina's estimator for the least eigenvalue and eigenvector.
Graph convolutional networks fail to use eigenvectors beyond the first, unlike spectral embedding.
problem Understanding when graph convolutional networks fail compared to spectral embedding.
method Presented a simple generative model to illustrate failure.
result Graph convolutional networks fail to use eigenvectors beyond the first in certain graphs.
We characterize the contractions that are similar to the backward shift in the Hardy space H2. This characterization is given in terms of the geometry of the eigenvector bundles of the operators.
We provide new examples of diffusion operators in dimension 2 and 3 which have orthogonal polynomials as eigenvectors. Their construction rely on the finite subgroups of O(3) and their invariant polynomials.
This paper solves a Calderón problem for Beltrami fields on manifolds.
problem Reconstructing a 3D manifold from boundary measurements of Beltrami fields.
method Defined a normal-to-tangential map for Beltrami fields and used it to reconstruct the manifold.
result A real-analytic 3-manifold can be reconstructed from its normal-to-tangential map.
A new spectral clustering algorithm that avoids eigenvector computation.
problem Computational complexity in spectral clustering for large datasets.
method A mixing process on a graph to find a linear combination of eigenvectors without computing eigenvectors.
result Partitioning datasets achieves better accuracy than standard spectral clustering methods.
Study eigenvalues and eigenvectors in neural networks, focusing on signal propagation.
problem Characterize signal eigenvalues and eigenvectors in neural networks.
method Characterizes signal eigenvalues and eigenvectors for a nonlinear spiked covariance model.
result Provides precise quantitative characterizations of signal eigenvalues and eigenvectors in neural networks.
The paper tackles learning symmetries in data without expert knowledge.
problem Learning symmetries in data from raw data without prior knowledge.
method Develops methods to select eigenvectors for orthogonal symmetries and compares their effectiveness.
result The problem of learning symmetries is as hard as the graph automorphism problem in the worst case, but can be simplified with certain restrictions.
New method improves covariance estimation for weighted samples.
problem Improving covariance estimation for weighted sample data.
method Asymptotic non-linear shrinkage formulas for covariance and precision matrix estimators of weighted sample covariances.
result Asymptotic non-linear shrinkage formulas for covariance and precision matrix estimators of weighted sample covariances.
Eigenvalue and eigenvector estimation improves with asymmetric data.
problem Estimating eigenvalues and eigenvectors from asymmetrically perturbed symmetric matrices.
method Eigenvalue and eigenvector analyses of asymmetrically perturbed low-rank matrices.
result The leading eigenvalue of the data matrix can be significantly more accurate than its singular value.
We apply random matrix theory to compare correlation matrix estimators C obtained from emerging market data. The correlation matrices are constructed from 10 years of daily data for stocks listed on the Johannesburg Stock Exchange (JSE) from January 1993 to December 2002. We test the spectral properties of C against ra…
Exact formulas for eigenvector overlaps in correlated random matrices.
problem Understanding overlaps between eigenvectors of correlated random matrices.
method Exact formulas derived for overlaps between eigenvectors of large correlated random matrices with additive or multiplicative noise.
result Overlaps only depend on measurable quantities and do not require knowledge of the noiseless matrices.