Semi-discrete Tikhonov regularization in RKHS with large randomly distributed noise

DOI10.1088/1361-6420/acdf16MaRDI QIDQ6050821

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Publication date: 12 October 2023

Published in: Inverse Problems (Search for Journal in Brave)

zbMATH Keywords

Tikhonov regularizationill-posed problemsreproducing kernel Hilbert spacerandom noise

Mathematics Subject Classification ID

Numerical approximation and computational geometry (primarily algorithms) (65Dxx) Approximations and expansions (41Axx) Numerical analysis in abstract spaces (65Jxx)

Cites Work

• Unnamed Item
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• Regularization theory for ill-posed problems. Selected topics
• Heuristic regularization methods for numerical differentiation
• Multiscale support vector approach for solving ill-posed problems
• The missing Wendland functions
• On the regularization of projection methods for solving ill-posed problems
• Risk estimators for choosing regularization parameters in ill-posed problems -- properties and limitations
• Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree
• Regularization in a functional reproducing kernel Hilbert space
• Extract the information from big data with randomly distributed noise
• On a heuristic stopping rule for the regularization of inverse problems by the augmented Lagrangian method
• Functional reproducing kernel Hilbert spaces for non-point-evaluation functional data
• Approximate interpolation with applications to selecting smoothing parameters
• Regularized minimal-norm solution of an overdetermined system of first kind integral equations
• On the mathematical foundations of learning
• Multiscale analysis for ill-posed problems with semi-discrete Tikhonov regularization
• Support vector regression for the solution of linear integral equations
• Linear inverse problems with discrete data: II. Stability and regularisation
• Efficient Methods for Data Smoothing
• The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems
• Sobolev bounds on functions with scattered zeros, with applications to radial basis function surface fitting
• The approximate inverse in action II: convergence and stability
• Approximate inverse for linear and some nonlinear problems
• Sobolev error estimates and a priori parameter selection for semi-discrete Tikhonov regularization
• Analysis of regularized Nyström subsampling for regression functions of low smoothness
• Multiscale Methods for Fredholm Integral Equations
• Discrete Inverse Problems
• Scattered Data Approximation
• Smoothing noisy data with spline functions
• A multiscale RBF method for severely ill-posed problems on spheres

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