主分量分析 principle component analysis ; PCA ; Robust KPCA ; PRINCOMP
核主分量分析 KPCA ; Kernel PCA ; Kernel Principal Component Analysis
模糊主分量分析 FPCA
非线性主分量分析 NPCA
模块主分量分析 M PCA ; modular PCA
主分量分析神经网络 PCANN ; PCA neural networks
粗糙主分量分析法 Rough PCA
PCA主分量分析 Primary component analyze
子模式主分量分析 Subpattern-based PCA ; SPPCA
增量减量主元分析 IDPCA
用主量分析(PCA)方法进行特征抽取，RS方法对特征信息进行预处理，约简后的特征作为网络输入，构成车牌字符识别网络。
Feature information is gathered with PCA, and it is preprocessed with RS way. Reduced feature is used as input of neural network, recognition network is composed with it.
传统的特征提取方法主要有：主分量分析、奇异值分解、投影追踪、自组织映射等。
Classical feature extraction methods include: Principle Component Analysis, Singular Value Decomposition, Projection Pursuit, Self-Organizing Map, and so on.
提出了一种基于主分量分析和属性距离和的孤立点检测算法。
An outlier detection algorithm based on principal component analysis and the sum of attributes distance is proposed.
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