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对UCI数据集之一进行PCA特征抽取实验,给出在二维PCA特征空间的数据散点图。,UCI data sets on one of PCA feature extraction experiments are given in the two-dimensional PCA feature space of the data scatter.
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快速的人脸特征提取算法KPCA，比普通的pca特征提取算法在效率上好了不少,Fast facial feature extraction algorithm KPCA, than ordinary PCA feature extraction algorithm in the efficiency of a good many
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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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人脸特征提取经典PCA方法的matlab源码，做这方面的朋友不妨试试。,Human Face Feature Extraction classic PCA method matlab source code, make friends in this area worth a try.
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pca人脸特征提取，可以根据需要提取不同维数的特征脸。,pca facial feature extraction, can extract the characteristics of the different dimensions of the face.
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用主成分分析法提取人脸图像特征的程序，算法理论依据是K-L变换,Principal Component Analysis with face image feature extraction process
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PCA,主成分分析，可应用于矩阵降维，人脸特征提取及人脸识别。-PCA, principal component analysis, can be applied to matrix reduction, facial feature extraction and face recognition.
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基于matlab的二维图像PCA特征提取-PCA feature extraction from image by matlab
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这是一个人脸识别的程序，先对图像预处理，然后用PCA进行特征提取。-This is a face recognition process, first on the image pre-processing, and then use PCA for feature extraction.
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子空间学习的代码，主要包括人脸识别中常用的特征提取算法如pca lda 以及目前常见的流行学习的相关代码-Subspace learning the code, mainly including commonly used in face recognition feature extraction algorithms such as pca lda and the current prevalence of common learning-related code
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应用PCA进行特征提取和降维，可以应用于数据挖掘，机器学习，人脸识别上！-Application of PCA for feature extraction and dimensionality reduction can be applied to data mining, machine learning, face recognition on!
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PCA主成分分析，用于人脸识别，特征提取等-PCA principal component analysis for face recognition, feature extraction, etc.
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子模式主成分分析首先对原始图像分块，然后对相同位置的子图像分别建立子图像集，在每一个子图像集内使用PCA方法提取特征，建立子空间。对待识别图像，经相同分块后，分别将子图像向对应的子空间投影，提取特征。最后根据最近邻原则进行分类。-Sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the
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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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pca特征提取子程序，毕设用的，算法优化非常好，几乎没有循环，相当节省时间，堪称经典，运行绝对正确-pca feature extraction subroutine, used to complete set up, very good algorithm, almost no circulation, saving considerable time, classic, run absolutely right! !
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pca 特征提取的源代码，对人脸识别很有帮助，-pca feature extraction of the source code, useful for face recognition,
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Image Feature Extraction based on PCA
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用MATLAB实现了基于pca的特征提取方法，有效地完成了识别，是识别的经典例子-Pca-based implementation using MATLAB feature extraction methods, efficient completion of the identification is to identify the classic example of
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pca又称主成分分析，主要用来提取图像的主要成分，作为特征提取一个重要算法，将其用于人脸识别-pca, also known as principal component analysis, mainly used to extract the main component of the image, as a key feature extraction algorithm, be used in face recognition
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