Principal manifold learning by sparse grids

DOI10.1007/s00607-009-0045-8zbMath1171.65054OpenAlexW2073043218MaRDI QIDQ836948

Christian Feuersänger, Michael Griebel

Publication date: 9 September 2009

Published in: Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00607-009-0045-8

zbMATH Keywords

numerical experimentsdescent methodexpectation maximization algorithmempirical quantization error functionalprincipal manifold approachsparse grid method

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Discrete approximations in optimal control (49M25) Existence theories for free problems in one independent variable (49J05)

Related Items (6)

Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection ⋮ Multiple penalized principal curves: analysis and computation ⋮ Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids ⋮ A sparse grid based method for generative dimensionality reduction of high-dimensional data ⋮ Fast Discrete Fourier Transform on Generalized Sparse Grids ⋮ A Hyperspherical Adaptive Sparse-Grid Method for High-Dimensional Discontinuity Detection

Uses Software

• GTM

Cites Work

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