Robust tensor analysis with l1-norm

Pang, Y. and Li, Xuelong and Yuan, Y. (2010) Robust tensor analysis with l1-norm. IEEE Transactions on Circuits and Systems for Video Technology 20 (2), pp. 172-178. ISSN 1051-8215.

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1109/TCSVT.2009.2020337

Abstract

Tensor analysis plays an important role in modern image and vision computing problems. Most of the existing tensor analysis approaches are based on the Frobenius norm, which makes them sensitive to outliers. In this paper, we propose L1-norm-based tensor analysis (TPCA-L1), which is robust to outliers. Experimental results upon face and other datasets demonstrate the advantages of the proposed approach.

Metadata

Item Type:	Article
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	20 Jun 2013 10:36
Last Modified:	09 Aug 2023 12:33
URI:	https://eprints.bbk.ac.uk/id/eprint/7522

Statistics

Archive Staff Only (login required)

Edit/View Item