Direct ICA on data tensor via random matrix modeling

Song, L., Zhou, S. orcid.org/0000-0002-8069-2814 and Lu, H. (2022) Direct ICA on data tensor via random matrix modeling. Signal Processing, 196. 108508. ISSN 0165-1684

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © 2022 Elsevier B.V. This is an author produced version of a paper subsequently published in Signal Processing. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Blind source separation; Independent component analysis; Tensor
Dates:
  • Accepted: 13 February 2022
  • Published (online): 18 February 2022
  • Published: July 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
FunderGrant number
Engineering and Physical Sciences Research CouncilEP/R014507/1
Depositing User: Symplectic Sheffield
Date Deposited: 25 Feb 2022 11:39
Last Modified: 18 Feb 2023 01:13
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.sigpro.2022.108508

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