Wavelet Frames to Optimally Learn Functions on Diffusion Measure Spaces

DOI10.1007/978-3-319-12577-0_78zbMath1330.42021OpenAlexW2303115273MaRDI QIDQ2949331

Martin Ehler, Frank-Dieter Filbir

Publication date: 8 October 2015

Published in: Trends in Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-12577-0_78

zbMATH Keywords

diffusion measure spacedata-defined manifold

Mathematics Subject Classification ID

Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Approximation by arbitrary nonlinear expressions; widths and entropy (41A46)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• n-widths of Sobolev spaces in \(L^ p\)
• Metric entropy, \(n\)-widths, and sampling of functions on manifolds
• On the representation of smooth functions on the sphere using finitely many bits
• Über die beste Annäherung von Funktionen einer gegebenen Funktionenklasse
• Smooth function extension based on high dimensional unstructured data

This page was built for publication: Wavelet Frames to Optimally Learn Functions on Diffusion Measure Spaces