Manifold learning with arbitrary norms
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Publication:1982603
DOI10.1007/s00041-021-09879-2OpenAlexW3197713830MaRDI QIDQ1982603
Amit Singer, Joe Kileel, Amit Moscovich, Nathan Zelesko
Publication date: 14 September 2021
Published in: The Journal of Fourier Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2012.14172
Riemannian geometrysecond-order differential operatorconvex bodydimensionality reductiondiffusion mapsLaplacian eigenmaps
Related Items (4)
Balancing Geometry and Density: Path Distances on High-Dimensional Data ⋮ Overview of the topical collection: harmonic analysis on combinatorial graphs ⋮ Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning ⋮ Applications of No-Collision Transportation Maps in Manifold Learning
Uses Software
Cites Work
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