An Out of Memory tSVD for Big-Data Factorization

An Out of Memory tSVD for Big-Data Factorization

Singular value decomposition (SVD) is a matrix factorization method widely used for dimension reduction, data analytics, information retrieval, and unsupervised learning. In general, only singular values of SVD are needed for most big-data applications. Methods such as tensor networks require an acc...

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Bibliographic Details
Main Authors: Hector Carrillo-Cabada, Erik Skau, Gopinath Chennupati, Boian Alexandrov, Hristo Djidjev
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
tSVD
out of memory
tensor train
singular vectors
tensor networks
Online Access:https://ieeexplore.ieee.org/document/9110583/

Internet

https://ieeexplore.ieee.org/document/9110583/

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