Analyzing Large and Sparse Tensor Data using Spectral Low-Rank Approximation

Autor:	Eldén, L., Dehghan, Maryam
Rok vydání:	2020
Předmět:	Mathematics - Numerical Analysis 05C50 (Primary) 15A69 65F15 (Secondary)
Druh dokumentu:	Working Paper
Popis:	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 tensor. The first method can be considered as a generalization of spectral graph partitioning to tensors, and it gives a reordering of the tensor that clusters the information. The second method gives an expansion of the tensor in sparse rank-(2,2,1) terms, where the terms correspond to graphs. The low-rank approximations are computed using an efficient Krylov-Schur type algorithm that avoids filling in the sparse data. The methods are applied to topic search in news text, a tensor representing conference author-terms-years, and network traffic logs. Comment: 28 pages, 27 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.07754 Zobrazit plný text záznamu View this record from Arxiv