Fitting multidimensional data using gradient penalties and the sparse grid combination technique

DOI10.1007/s00607-009-0027-xzbMath1166.65004OpenAlexW2068953972MaRDI QIDQ1014352

Markus Hegland, Jochen Garcke

Publication date: 27 April 2009

Published in: Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00607-009-0027-x

zbMATH Keywords

numerical examplessparse gridsregularisationregressioncombination techniqueleast squares fittinghigh dimensional data

Mathematics Subject Classification ID

Related Items (11)

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Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
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• The combination technique and some generalisations
• Regularisierung schlecht gestellter Probleme durch Projektionsverfahren
• Elliptic partial differential equations of second order
• Extrapolation, combination, and sparse grid techniques for elliptic boundary value problems
• Arbitrary discontinuities in finite elements
• Some Estimates for a Weighted L 2 Projection
• Data mining with sparse grids

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