Maximum neighborhood margin discriminant projection for classification.
We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data. It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters. By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes. To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA. The experimental results demonstrate the effectiveness of our MNMDP in pattern classification.
School of Computer Science and Telecommunication Engineering, JiangSu University, ZhenJiang, JiangSu 212013, China.
Jianping
0000-0003-1413-0693
J
Gou
School of Computer Science and Telecommunication Engineering, JiangSu University, ZhenJiang, JiangSu 212013, China.
Yongzhao
Y
Zhan
School of Mathematics and Computer Engineering, Xihua University, Chengdu, Sichuan 610039, China.
Min
0000-0002-3240-163X
M
Wan
School of Computer Science and Telecommunication Engineering, JiangSu University, ZhenJiang, JiangSu 212013, China.
Xiangjun
X
Shen
School of Computer Science and Telecommunication Engineering, JiangSu University, ZhenJiang, JiangSu 212013, China.
Jinfu
0000-0002-3124-5452
J
Chen
Department of Computing, Macquarie University, Sydney, NSW 2109, Australia.
Lan
L
Du
D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000145:classification*; D016002:Discriminant Analysis