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现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。-existing algebra feature extraction method using a majority of th
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统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines,
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用来进行主成分分析，实现数据压缩功能，也可以做特征提取与分类-Be used for principal component analysis, data compression, you can also do feature extraction and classification
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主成分分析程序，可用于数据降维及特征提取。-Principal component analysis procedures, can be used for data dimensionality reduction and feature extraction.
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该程序包实现了模式识别中的两个特征提取算法，主成分分析PCA和线性判别分析LDA。采用C++语言编写，开发环境VS。 程序包还提供了两个测试样本文件。-The package to achieve the recognition of the two feature extraction algorithm, principal component analysis PCA and linear discriminant analysis LDA. Using C++ language, dev
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基于主元分析（pca）的人脸特征提取MATLAB实现。
-Based on principal component analysis (pca) Face Feature Extraction MATLAB implementation.
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核主成分分析方法，是主成分分析的一种改进算法，是一种非线性的特征提取方法。
-Kernel principal component analysis, is the principal component analysis of an improved algorithm, is a nonlinear feature extraction method.
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这是一个模式识别中关于主成分分析的特征提取的matlab源码 -This is a pattern recognition principal component analysis on the feature extraction matlab source
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matlab编写的动态主成分分析特征提取 实验有效-matlab dynamic principal component analysis prepared by the Feature Extraction effective
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在模式识别中，经常用到的一种提取特征的方法——主成分分析法-In pattern recognition, a frequently used feature extraction method- principal component analysis
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论文使用一种经典的特征提取方法—主成分分析法（PCA）进行特征提取，其基本思想是降维。降维后的数据除了计算工作量减少之外不会减少原始数据所包含的有效信息量。-This paper use a classical method for feature extraction—Principal Component Analysis(PCA)with the basic idea of dimensionality reduction(it still contains all valid infor
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PCA 主成分分析 特征抽取 特征降维 matlab实现-PCA principal component analysis feature extraction dimension reduction
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主分量分析 和 核主分量分析的 原理简介，主分量分析（PCA）用于对信号进行特征提取和降维-Introduction of the principle of the principal component analysis and kernel principal component analysis, principal component analysis (PCA) for feature extraction and dimensionality reduction of signal
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主成分分析特征提取，K近邻目标识别（针对图像）。-The principal component analysis feature extraction, the K neighboring target recognition (for images).
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主成份分析在模式识别中是一种特征提取方法！-a very important technique feature extraction in pattern recognazition
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Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensiona
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A new method for performing a nonlinear form of Principal
Component Analysis proposed. By the use of integral operator kernel
functions, one can eciently compute principal components in high{
dimensional feature spaces, related to input space
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PCA:主元分析。进行降维，实现TE化工过程特征提取。-PCA: principal component analysis. Dimensionality reduction, feature extraction chemical process to achieve TE.
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主成份分析例程，适合初学者研究，用于特征提取和分类识别。-Principal component analysis routines, suitable for beginners, for feature extraction and classification.
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Nowadays security becomes a most important issue regarding a spoof attack. So, multimodal biometrics technology has attracted
substantial interest for its highest user acceptance, high security, high accuracy, low spoof attack and high recognition
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