• DocumentCode
    1382121
  • Title

    L1-Norm-Based 2DPCA

  • Author

    Li, Xuelong ; Pang, Yanwei ; Yuan, Yuan

  • Author_Institution
    State Key Lab. of Transient Opt. & Photonics, Chinese Acad. of Sci., Xi´´an, China
  • Volume
    40
  • Issue
    4
  • fYear
    2010
  • Firstpage
    1170
  • Lastpage
    1175
  • Abstract
    In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.
  • Keywords
    feature extraction; image reconstruction; principal component analysis; L1-norm-based 2DPCA; L2-norm-based least squares criterion; two-dimensional principal component analysis; L1 norm; outlier; subspace; two-dimensional principal component analysis (2DPCA); Algorithms; Artificial Intelligence; Computer Simulation; Image Enhancement; Image Interpretation, Computer-Assisted; Models, Statistical; Pattern Recognition, Automated; Principal Component Analysis; Reproducibility of Results; Sensitivity and Specificity;
  • fLanguage
    English
  • Journal_Title
    Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1083-4419
  • Type

    jour

  • DOI
    10.1109/TSMCB.2009.2035629
  • Filename
    5382548
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1382121