1 citations · 1 across the 3 of their papers we have counts for
3 papers
math.NA2020
Spectral Partitioning of Large and Sparse Tensors using Low-Rank Tensor Approximation
Lars Eldén, Maryam Dehghan
The problem of partitioning a large and sparse tensor is considered, where the tensor consists of a sequence of adjacency matrices. Theory is developed that is a generalization of…
math.NA2020
A Krylov-Schur like method for computing the best rank- approximation of large and sparse tensors
L. Eldén, M. Dehghan
The paper is concerned with methods for computing the best low multilinear rank approximation of large and sparse tensors. Krylov-type methods have been used for this problem; here…
math.NA2020★ 1 cited
Analyzing Large and Sparse Tensor Data using Spectral Low-Rank Approximation
L. Eldén, Maryam Dehghan
Information is extracted from large and sparse data sets organized as 3-mode tensors. Two methods are described, based on best rank-(2,2,2) and rank-(2,2,1) approximation of the te…