Isotropic sparse regularization for spherical harmonic representations of random fields on the sphere
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Publication:2175023
DOI10.1016/j.acha.2019.01.005zbMath1462.60064arXiv1801.03212OpenAlexW2914163624WikidataQ128590827 ScholiaQ128590827MaRDI QIDQ2175023
Ian H. Sloan, Yu Guang Wang, Robert S. Womersley, Quoc Thong Le Gia
Publication date: 27 April 2020
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1801.03212
Related Items (7)
Numerical approximation and simulation of the stochastic wave equation on the sphere ⋮ Continuous regularized least squares polynomial approximation on the sphere ⋮ High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity terms ⋮ Lasso estimation for spherical autoregressive processes ⋮ Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere ⋮ Group Sparse Optimization for Images Recovery Using Capped Folded Concave Functions ⋮ Isotropic non-Lipschitz regularization for sparse representations of random fields on the sphere
Uses Software
Cites Work
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- Needlet approximation for isotropic random fields on the sphere
- Accelerated projected gradient method for linear inverse problems with sparsity constraints
- Isotropic Gaussian random fields on the sphere: regularity, fast simulation and stochastic partial differential equations
- Regularity properties and simulations of Gaussian random fields on the sphere cross time
- Fast generation of isotropic Gaussian random fields on the sphere
- Least angle regression. (With discussion)
- The stochastic properties of \(\ell^1\)-regularized spherical Gaussian fields
- Spherical harmonics
- Random Fields on the Sphere
- Sparse Optimization with Least-Squares Constraints
- Probing the Pareto Frontier for Basis Pursuit Solutions
- A new approach to variable selection in least squares problems
- Sparse Reconstruction by Separable Approximation
- A necessary and sufficient condition for strictly positive definite functions on spheres
- Asymmetric matrix-valued covariances for multivariate random fields on spheres
- For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution
- Stable signal recovery from incomplete and inaccurate measurements
- Modeling Temporally Evolving and Spatially Globally Dependent Data
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