Sliced-Inverse-Regression--Aided Rotated Compressive Sensing Method for Uncertainty Quantification (Q4611526)

scientific article; zbMATH DE number 7003645

Language	Label	Description	Also known as
English	Sliced-Inverse-Regression--Aided Rotated Compressive Sensing Method for Uncertainty Quantification	scientific article; zbMATH DE number 7003645

Statements

instance of

scholarly article

0 references

title

Sliced-Inverse-Regression--Aided Rotated Compressive Sensing Method for Uncertainty Quantification (English)

0 references

author

Xiu Yang

0 references

Weixuan Li

0 references

Alexandre M. Tartakovsky

0 references

published in

SIAM/ASA Journal on Uncertainty Quantification

0 references

publication date

21 January 2019

0 references

full work available at URL

https://arxiv.org/abs/1709.07937

0 references

zbMATH Keywords

compressive sensing

0 references

uncertainty quantification

0 references

sliced inverse regression

0 references

iterative rotation

0 references

alternating direction method

0 references

describes a project that uses

glm

0 references

SPGL1

0 references

MaRDI profile type

MaRDI publication profile

0 references

cites work

Infinite-dimensional compressed sensing and function interpolation

0 references

A new look at the statistical model identification

0 references

Divide and conquer: an incremental sparsity promoting compressive sampling approach for polynomial chaos expansions

0 references

A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions

0 references

A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

0 references

From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images

0 references

The orthogonal development of non-linear functionals in series of Fourier-Hermite functionals

0 references

The restricted isometry property and its implications for compressed sensing

0 references

Stable signal recovery from incomplete and inaccurate measurements

0 references

Decoding by Linear Programming

0 references

Enhancing sparsity by reweighted $\ell _{1}$ minimization

0 references

Erratum: Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces

0 references

Comment

0 references

Compressed sensing

0 references

Stable recovery of sparse overcomplete representations in the presence of noise

0 references

A non-adapted sparse approximation of PDEs with stochastic inputs

0 references

On the convergence of generalized polynomial chaos expansions

0 references

The multi-element probabilistic collocation method (ME-PCM): Error analysis and applications

0 references

Q3997987

0 references

Compressive sampling of polynomial chaos expansions: convergence analysis and sampling strategies

0 references

Basis adaptive sample efficient polynomial chaos (BASE-PC)

0 references

Q3416771

0 references

Enhancing $\ell_1$-minimization estimates of polynomial chaos expansions using basis selection

0 references

Variable selection for general index models via sliced inverse regression

0 references

A Bayesian mixed shrinkage prior procedure for spatial-stochastic basis selection and evaluation of gPC expansions: applications to elliptic SPDEs

0 references

Constructing Surrogate Models of Complex Systems with Enhanced Sparsity: Quantifying the Influence of Conformational Uncertainty in Biomolecular Solvation

0 references

Sliced Inverse Regression for Dimension Reduction

0 references

Sparse sufficient dimension reduction

0 references

Inverse regression-based uncertainty quantification algorithms for high-dimensional models: theory and practice

0 references

ORTHOGONAL BASES FOR POLYNOMIAL REGRESSION WITH DERIVATIVE INFORMATION IN UNCERTAINTY QUANTIFICATION

0 references

Numerical studies of the stochastic Korteweg-de Vries equation

0 references

A weighted $\ell_1$-minimization approach for sparse polynomial chaos expansions

0 references

On polynomial chaos expansion via gradient-enhanced $\ell_1$-minimization

0 references

Sparse Legendre expansions via $\ell_1$-minimization

0 references

Interpolation via weighted $\ell_{1}$ minimization

0 references

On sparse reconstruction from Fourier and Gaussian measurements

0 references

DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING

0 references

Estimating the dimension of a model

0 references

Q5600030

0 references

Basis adaptation in homogeneous chaos spaces

0 references

Probing the Pareto Frontier for Basis Pursuit Solutions

0 references

A constructive approach to the estimation of dimension reduction directions

0 references

An Adaptive Estimation of Dimension Reduction Space

0 references

High-Order Collocation Methods for Differential Equations with Random Inputs

0 references

The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

0 references

STOCHASTIC COLLOCATION ALGORITHMS USING l1-MINIMIZATION

0 references

Adaptive ANOVA decomposition of stochastic incompressible and compressible flows

0 references

Reweighted $\ell_1$ minimization method for stochastic elliptic differential equations

0 references

Enhancing sparsity of Hermite polynomial expansions by iterative rotations

0 references

Minimization of $\ell_{1-2}$ for Compressed Sensing

0 references

Identifiers

DOI

10.1137/17M1148955

0 references

Mathematics Subject Classification ID

0 references

0 references

0 references

0 references

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:4611526