Relative principle component and relative principle component analysis algorithm

被引:0
|
作者
Wen, Cheng-Lin [1 ]
Hu, Jing [2 ]
Wang, Tian-Zhen [3 ]
机构
[1] Hangzhou Dianzi Univ, Inst Informat & Control, Hangzhou 310018, Peoples R China
[2] Henan Univ, Dept Comp Sci & Engn, Kaifeng 475001, Peoples R China
[3] Shanghai Maritime Univ, Dept Elect Automat, Shanghai 200135, Peoples R China
来源
ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 2, PROCEEDINGS | 2007年 / 4492卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Aiming at the problems happened in the practical application of traditional Principle Component Analysis (PCA), the concept of Relative Principle Component (RPC) and method of Relative Principle Component Analysis (RPCA) are put forward. Meanwhile, some concepts such as Relative Transform (RT), "Rotundity" Scatter and so on are introduced. The new algorithm can overcome some disadvantages of traditional PCA for compressing data when data is "Rotundity" Scatter. A simulation has been used to demonstrate the effectiveness and practicability of the algorithm proposed. The RPCs selected by RPCA are more representative, and the way to choose RPCs is more flexible, so that the application of the new algorithm will be very extensive.
引用
收藏
页码:985 / +
页数:2
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