The Computation of Low Multilinear Rank Approximations of Tensors via Power Scheme and Random Projection
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Publication:5112244
DOI10.1137/19M1237016OpenAlexW3021574402WikidataQ114074268 ScholiaQ114074268MaRDI QIDQ5112244
Hong Yan, Yi-Min Wei, Mao-Lin Che
Publication date: 28 May 2020
Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/19m1237016
singular value decompositionsingular valuesrandomized algorithmsrandom projectionlow multilinear rank approximationpower schemerandom sub-Gaussian matrices
Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Eigenvalues, singular values, and eigenvectors (15A18) Iterative numerical methods for linear systems (65F10) Algorithms for approximation of functions (65D15) Multilinear algebra, tensor calculus (15A69) Randomized algorithms (68W20) Numerical approximation of high-dimensional functions; sparse grids (65D40)
Related Items (12)
On approximation algorithm for orthogonal low-rank tensor approximation ⋮ Perturbations of the \textsc{Tcur} decomposition for tensor valued data in the Tucker format ⋮ An efficient algorithm for computing the approximate t-URV and its applications ⋮ Tensor CUR Decomposition under T-Product and Its Perturbation ⋮ Solving coupled tensor equations via higher order LSQR methods ⋮ Randomized algorithms for the computation of multilinear rank-\((\mu_1,\mu_2,\mu_3)\) approximations ⋮ Practical sketching algorithms for low-rank Tucker approximation of large tensors ⋮ Provable stochastic algorithm for large-scale fully-connected tensor network decomposition ⋮ Optimality conditions for Tucker low-rank tensor optimization ⋮ A randomized singular value decomposition for third-order oriented tensors ⋮ An efficient randomized algorithm for computing the approximate Tucker decomposition ⋮ Tensor Bi-CR Methods for Solutions of High Order Tensor Equation Accompanied by Einstein Product
Uses Software
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