Diffusion-based kernel methods on Euclidean metric measure spaces
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Publication:285541
DOI10.1016/j.acha.2015.07.005zbMath1382.42014OpenAlexW2102346443MaRDI QIDQ285541
Amit Bermanis, Guy Wolf, Amir Z. Averbuch
Publication date: 19 May 2016
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.acha.2015.07.005
spectral analysiskernel methodsmanifold learningdiffusion mapsdiffusion-based kernelmeasure-based information
Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Harmonic analysis and PDEs (42B37)
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Cites Work
- Unnamed Item
- Anisotropic diffusion on sub-manifolds with application to Earth structure classification
- Hierarchical data organization, clustering and denoising via localized diffusion folders
- Diffusion representations
- Measure-based diffusion grid construction and high-dimensional data discretization
- Multiscale data sampling and function extension
- Principal component analysis.
- Bi-stochastic kernels via asymmetric affinity functions
- Texture separation via a reference set
- Diffusion maps
- Geometric harmonics: a novel tool for multiscale out-of-sample extension of empirical functions
- Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
- Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
- Diffusion Maps, Reduction Coordinates, and Low Dimensional Representation of Stochastic Systems
- Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- The Kernel Recursive Least-Squares Algorithm
- Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
