Weighted random sampling and reconstruction in general multivariate trigonometric polynomial spaces
DOI10.1142/S0219530521500330zbMath1492.94047OpenAlexW4205843933MaRDI QIDQ5089731
Publication date: 15 July 2022
Published in: Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219530521500330
reconstruction formularandom samplingcovering numberssampling inequalitymultivariate trigonometric polynomial spaces
Sampling theory, sample surveys (62D05) Orthogonal functions and polynomials, general theory of nontrigonometric harmonic analysis (42C05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Sampling theory in information and communication theory (94A20)
Related Items (2)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Wiener-Ingham type inequality for Vilenkin groups and its application to harmonic analysis
- Random sampling of bandlimited functions
- Relevant sampling in finitely generated shift-invariant spaces
- Random sampling in shift invariant spaces
- Convolution, average sampling, and a Calderon resolution of the identity for shift-invariant spaces
- Random sampling of sparse trigonometric polynomials
- Characterization of local sampling sequences for spline subspaces
- Riesz bases of exponentials and sine-type functions
- Fourier reconstruction of functions from their nonstandard sampled Radon transform
- Non-uniform weighted average sampling and reconstruction in shift-invariant and wavelet spaces
- Nonuniform average sampling and reconstruction in multiply generated shift-invariant spaces
- Function values are enough for \(L_2\)-approximation. II
- \(L_2\)-norm sampling discretization and recovery of functions from RKHS with finite trace
- Random sampling and approximation of signals with bounded derivatives
- On the worst-case error of least squares algorithms for \(L_2\)-approximation with high probability
- Function values are enough for \(L_2\)-approximation
- Universality of deep convolutional neural networks
- Local reconstruction for sampling in shift-invariant spaces
- Sampling set conditions in weighted multiply generated shift-invariant spaces and their applications
- A sampling theory for non-decaying signals
- Approximation of non-decaying signals from shift-invariant subspaces
- Quantifying invariance properties of shift-invariant spaces
- Relevant sampling of band-limited functions
- On the mathematical foundations of learning
- Nonuniform Sampling and Reconstruction in Shift-Invariant Spaces
- Convolution sampling and reconstruction of signals in a reproducing kernel subspace
- Local sampling theorems for spaces generated by splines with arbitrary knots
- Probability Inequalities for the Sum of Independent Random Variables
- Learning Theory
- ONLINE LEARNING WITH MARKOV SAMPLING
- Nonharmonic fourier series and the stabilization of distributed semi-linear control systems
- Deep distributed convolutional neural networks: Universality
- Random Sampling of Multivariate Trigonometric Polynomials
- Fourier Series in Control Theory
- Benign overfitting in linear regression
- Reconstruction from convolution random sampling in local shift invariant spaces
- Random sampling and reconstruction in multiply generated shift-invariant spaces
- On lacunary non-harmonic trigonometric series
This page was built for publication: Weighted random sampling and reconstruction in general multivariate trigonometric polynomial spaces