New diffusion operators found using group theory.
problem Finding diffusion operators with polynomial eigenvectors.
method Finite subgroups of O(3) and invariant polynomials.
result Orthogonal polynomials as eigenvectors for diffusion operators.
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48 results for “polynomial eigenvectors”
Given an invariant J(K) of a knot K, the corresponding (1,1)-tangle invariant J'(K)=J(K)/J(U) is defined as the quotient of J(K) by its value J(U) on the unknot U. We prove here that J' is always an integer 2-variable Laurent polynomial when J is the Homfly satellite invariant determined by decorating K with any eigenv…
Two new algorithms improve robust PCA and Schatten packing.
problem Robustly estimating the top eigenvector of corrupted sub-Gaussian data.
method Two iterative filtering and nearly-linear time algorithms.
result First polynomial-time algorithms for non-trivial covariance estimation.
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.
Sign equivariant networks improve model expressiveness for spectral geometric learning.
problem Limited expressiveness of sign invariant models for tasks like graph link prediction.
method Developed sign equivariant neural network architectures based on new analytic sign equivariant polynomials.
result Sign equivariant models achieve theoretical benefits in spectral geometric learning tasks.
GRAMPA spectral method solves graph matching problem with high probability.
problem Finding vertex correspondence between unlabeled graphs.
method GRAMPA constructs a similarity matrix from weighted eigenvector comparisons, rounding to produce a matching.
result GRAMPA exactly recovers correct vertex correspondence with high probability for Gaussian models.
Lower bounds show linear complexity for linear regression.
problem Computational complexity of linear regression.
method Reduction to estimating the least eigenvalue of a random Wishart matrix.
result Θ(d) calls to the oracle are necessary and sufficient for polynomial accuracy.
Analysis of DPPs and k-DPPs via spectral decomposition reveals identifiable parameters and non-identifiability gaps.
problem Identifying parameters of DPPs and k-DPPs through spectral decomposition.
method Spectral decomposition of the covariance matrix, analysis of invariances, and counting arguments.
result Identifiability of parameters changes fundamentally for k-DPPs, with specific invariances and non-identifiability gaps.
In recent years, sparse principal component analysis has emerged as an extremely popular dimension reduction technique for high-dimensional data. The theoretical challenge, in the simplest case, is to estimate the leading eigenvector of a population covariance matrix under the assumption that this eigenvector is sparse…
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.
Study the spectrum of Poincaré operator in triaxial ellipsoids.
problem Spectrum of the Poincaré operator in triaxial ellipsoids.
method Microlocal analysis of partial differential equations and polynomial vector fields.
result Polynomial eigenvectors and large-degree asymptotics of the operator.
The paper explores how polynomial roots and operator eigenvalues change with parameters.
problem How do roots of polynomials and eigenvalues of operators vary with parameter changes?
method Analyzes parameter dependence of polynomials and linear operators, covering real analytic to differentiable of finite order.
result Definitive optimal results for perturbation theory of polynomials and linear operators, including hyperbolic polynomials.
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.
Paper finds exact relationship between adjacency and modularity eigenvectors.
problem Understanding the relationship between adjacency and modularity in graph partitioning.
method Developed exact linear relationship and derived error of approximation for leading eigenvector of modularity matrix.
result Normalized adjacency clustering can be twice as efficient as normalized modularity clustering.
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.
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 , allowing for embedding into a finite-dimensional Euclidean space.
Researchers prove Lie algebras of differential operators and Grothendieck constructions coincide.
problem Understanding derivations and Lie algebras of vector bundles.
method Proving Lie algebras coincide through differential operators and Grothendieck constructions.
result Lie algebras coincide up to an isomorphism.
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 method controls linear systems with adversarial disturbances.
problem Controlling linear dynamical systems under adversarial conditions.
method Novel convex relaxation using spectral filters from Hankel matrix eigenvectors.
result Polylogarithmic running time improvement over prior methods.
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.
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.
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 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.
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.
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.
New method improves subspace iteration for eigenvectors in machine learning.
problem Computing eigenvectors for large-scale problems in machine learning.
method Subspace iteration with norm convergence analysis.
result Deterministic bounds and practical stopping criterion for improved performance.
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.
Perturbation of the Eigenvectors of the Graph Laplacian: Application to Image Denoisingphysics.data-an
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 . This characterization is given in terms of the geometry of the eigenvector bundles of the operators.
Efficient algorithm predicts discrete-time linear systems using spectral filtering.
problem Online prediction of discrete-time linear dynamical systems.
method Improper learning to convexify the loss functions, then using spectral filtering.
result Near-optimal regret and sample complexity guarantees for agnostic learning.
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.
Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed Low-Rank Matricesmath.ST
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.