L1-norm-based 2DPCA

IEEE Trans Syst Man Cybern B Cybern. 2010 Aug;40(4):1170-5. doi: 10.1109/TSMCB.2009.2035629. Epub 2010 Jan 15.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Computer Simulation
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Models, Statistical
  • Pattern Recognition, Automated / methods*
  • Principal Component Analysis
  • Reproducibility of Results
  • Sensitivity and Specificity