A sparse grid based method for generative dimensionality reduction of high-dimensional data

DOI10.1016/j.jcp.2015.12.033zbMath1351.62120OpenAlexW2202181264MaRDI QIDQ729476

Michael Griebel, Bastian Bohn, Jochen Garcke

Publication date: 20 December 2016

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2015.12.033

zbMATH Keywords

sparse gridsmachine learningdimensionality reductiongenerative modelscar-crash analysisnumerical simulation data

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Learning and adaptive systems in artificial intelligence (68T05) Analysis of variance and covariance (ANOVA) (62J10)

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Uses Software

• GTM

Cites Work

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• A distribution-free theory of nonparametric regression
• Dimensionality Reduction of High-Dimensional Data with a NonLinear Principal Component Aligned Generative Topographic Mapping
• Learning Theory
• 10.1162/15324430152748227
• ENABLING THE ANALYSIS OF FINITE ELEMENT SIMULATION BUNDLES
• Sparse grids
• Nonlinear Dimensionality Reduction
• On Learning Vector-Valued Functions

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