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