Cover-based bounds on the numerical rank of Gaussian kernels

DOI10.1016/j.acha.2013.05.004zbMath1294.65058OpenAlexW2072409701MaRDI QIDQ2252212

Amit Bermanis, Guy Wolf, Amir Z. Averbuch

Publication date: 16 July 2014

Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.acha.2013.05.004

zbMATH Keywords

eigenvalueeigenvectorkernel methodspectral analysisdimensionality reductionnumerical rankGaussian convolution operatornon-parametric affinity kernelsspectral manifold-learning method

Mathematics Subject Classification ID

General theory of numerical analysis in abstract spaces (65J05) Spectrum, resolvent (47A10)

Related Items (3)

Diffusion representations ⋮ Geometric component analysis and its applications to data analysis ⋮ Fast and Accurate Gaussian Kernel Ridge Regression Using Matrix Decompositions for Preconditioning

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Multiscale data sampling and function extension
• Principal component analysis.
• Consistency of spectral clustering
• Diffusion maps
• Geometric harmonics: a novel tool for multiscale out-of-sample extension of empirical functions
• Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
• Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
• Scattered Data Approximation

This page was built for publication: Cover-based bounds on the numerical rank of Gaussian kernels