Robust tensor analysis with L1-norm

Yanwei Pang*, Xuelong Li, Yuan Yuan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)172-178
Number of pages7
JournalIEEE Transactions on Circuits and Systems For Video Technology
Issue number2
Early online date7 Apr 2009
Publication statusPublished - Feb 2010


  • L1-norm
  • outlier
  • tensor analysis


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