Pseudo-skeleton approximations with better accuracy estimates

From MaRDI portal
Publication:1675666

DOI10.1016/j.laa.2017.09.032zbMath1376.65073OpenAlexW2763005113MaRDI QIDQ1675666

A. I. Osinsky, Nikolai L. Zamarashkin

Publication date: 2 November 2017

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.laa.2017.09.032




Related Items

On the best approximation algorithm by low-rank matrices in Chebyshev's normPerturbations of the \textsc{Tcur} decomposition for tensor valued data in the Tucker formatTensor CUR Decomposition under T-Product and Its PerturbationRectangular maximum-volume submatrices and their applicationsA note on error bounds for pseudo skeleton approximations of matricesA literature survey of matrix methods for data sciencePolynomial time \(\rho\)-locally maximum volume searchLow complexity matrix projections preserving actions on vectorsLower bounds for column matrix approximationsOn the distance to low-rank matrices in the maximum normGlobal optimization based on TT-decompositionLow Rank Structures in Solving Electromagnetic ProblemsA two-stage surrogate model for neo-Hookean problems based on adaptive proper orthogonal decomposition and hierarchical tensor approximationMethods for nonnegative matrix factorization based on low-rank cross approximationsOn the existence of a nearly optimal skeleton approximation of a matrix in the Frobenius normNew applications of matrix methodsOn the accuracy of cross and column low-rank maxvol approximations in averageLow-rank approximation algorithms for matrix completion with random samplingLinear-time CUR approximation of BEM matricesLow rank methods of approximation in an electromagnetic problemTensor trains approximation estimates in the Chebyshev normFast and Accurate Gaussian Kernel Ridge Regression Using Matrix Decompositions for PreconditioningPerturbations of CUR DecompositionsRobust CUR Decomposition: Theory and Imaging ApplicationsCUR LRA at Sublinear Cost Based on Volume Maximization



Cites Work