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模式识别PCA+LDA的C++源代码，用于图像的主分量分析-pattern recognition PCA LDA C source code for the image of Principal Component Analysis
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此程序为用于主成分分析的matlab程序，可以输出贡献率及画出二维散点图-this procedure for the Principal Component Analysis Matlab procedures can export contribution rate and the two-dimensional plot paint
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这里面包含两个文件，可以实现主成分分析（PCA）算法和线性判别分析（LDA）算法。不过还需要CMatrix类的定义。-which contained two documents, can be achieved principal component analysis (PCA) algorithm and linear discriminant analysis (LDA) algorithm. But also need CMatrix class definition.
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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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pca主成分分析算法matlab源码，利用matlab实现pca算法。-pca principal component analysis algorithm Matlab source, using Matlab achieve pca algorithm.
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pca进行特征提取源码，用matlab语言编写，pca即主成分分析-pca source for feature extraction using Matlab language, pca that Principal Component Analysis
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结合PCA+LDA的图像识别算法VC封装类,PCA（主元素分析，光照敏感）,可用于人脸识别的初级算法-combination of image recognition algorithm VC Packaging category, PCA (principal component analysis, Light-sensitive), can be used for the initial face recognition algorithm
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应用PCA（主成分分析）进行人脸识别的matlab程序，有较高成功率-PCA (Principal Component Analysis) face recognition Matlab procedures, which have a higher success rate
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主成分分析算法（PCA），这是一个外国人编写的，很具有参考价值-principal component analysis algorithm (PCA), which was prepared by a foreigner, it is very valuable reference
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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 on the Principal Component Analysis Feature Extraction of Matlab FOSS
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PCA and PLS aims:to get some
insight into the bilinear factor models Principal Component Analysis
(PCA) and Partial Least Squares (PLS) regression, focusing on the
mathematics and numerical aspects rather than how s and why s of
data analysis
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A Tutorial on Principal Component Analysis.Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that
is widely used but poorly understood. The goal of this paper is to dispel the magic behind this
black box.
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pca人脸识别This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database. Images are not
preprocessed and it is up to the user to preprocess the images as wanted,
not changing the fil
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利用Matlab编程实现主成分分析,从数学角度来看，这是一种降维处理技术-Using Matlab programming principal component analysis, from a mathematical point of view, this is a dimension reduction process technology
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这是PCA主分量分析在matlab中的基本应用，可以理解和学习体会下,This is the principal component analysis PCA in matlab basic applications, and learning experience can be understood under the
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C语言编写的PCA程序，实现主成分分析功能-C language of the PCA program to achieve the principal component analysis function
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降维工具箱，包含主元分析（PCA）,核主元分析（KPCA）等。-Dimensionality reduction kit, including principal component analysis (PCA), Kernel Principal Component Analysis (KPCA) and so on.
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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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选择“BreastCancer”数据集，使用支持向量机(SVM)对其进行分类。作为对比，第一次对特征集直接进行支持向量机分类，第二次对特征集进行主成分分析法的特征提取后，再对特征提取后的特征集进行支持向量机分类。并且对比和分析了两次分类的结果。(The BreastCancer data set is selected and classified by Support Vector Machine (SVM). For comparison, the first time the featur
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利用主成分分析法结合粒子群（PSO）优化极限学习机(ELM)进行工程费用估计预测(In this paper, principal component analysis (PCA) combined with particle swarm optimization (PSO) optimization extreme learning machine (ELM) is used to estimate and forecast engineering cost)
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