会议专题

L1-Norm-Based 2DLPP

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.

L1 norm 2DLPP outliers two dimensional projections

Hao-Xin Zhao Hong-Jie Xing Xi-Zhao Wang Jun-Fen Chen

Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Baoding, 071002, China

国际会议

2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

四川绵阳

英文

1259-1264

2011-05-23(万方平台首次上网日期,不代表论文的发表时间)