Identification information analysis of sample train set subspace

被引：0
|
作者：
Fu, XiangFei [1 ]
Zhou, Jiliu [1 ]
Lang, FangNian [2 ]
机构：
[1] Sichuan Univ, Sch Comp Sci Software, Chengdu 610064, Peoples R China
[2] Sichuan Univ, Sch Elect Informat, Chengdu 610064, Peoples R China
来源：
PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON IMAGE AND GRAPHICS | 2007年
关键词：
D O I：
10.1109/ICIG.2007.167
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Principal Component Analysis (PCA) which is widely used in pattern recognition field aims at reducing the dimension of sample. PCA replaces variables in the original sample vectors that have redundant information with fewer integrative variables. The recognition ability used authors algorithm is tested in the paper. It is proved that zerospace do not include any identification information which would be useful for distinguishing different samples. Experiment results based of our lab's facebase and ORL facebase shows the theory is right.
引用
收藏
页码：633 / +
页数：2
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