L1-Norm-Based 2DLPP
Haoxin Zhao, Hong-Jie Xing, Xizhao Wang, Junfen Chen · 2011
In this paper, we propose a new L1-Norm-Based two-dimensional locality preserving projections (2DLPP-L1). Traditional 2D-LPP can preserve local structure and extract feature directly form matrices, which shows great advantages. However, it is based on L2 norm. It is well known that L2-norm-based criterion is sensitive to outliers. We generalize 2D-LPP to its corresponding L1-norm-based version, i.e. 2DLPP-L1, which is more robust against outliers. To evaluate the performance of 2DLPP-L1, several experiments are performed on the ORL face databases. Experimental results demonstrate that 2DLPP-L1 has better performance than its related methods.