搜索资源列表
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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程序,处理绿萝的数据,机器学习,进行主成分分析,提取指定重要特征的方法。(PCA program, data processing ,Scindapsus, machine learning, principal component analysis method, A method of specifying important features .)
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主成分分析,贡献率排序,各成分贡献率,累计贡献率。自己输入数据矩阵和想要的主成分个数,有大量中文注释(Principal component analysis, ranking of contribution rate, contribution rate of each component, and cumulative contribution rate. You enter the data matrix and the number of the principal components
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opencv下,用PCA主成分分析的方式做人脸识别(Using PCA principal component analysis to do face recognition)
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主成分分析,提取图像中的SIFT特征点,用于图像识别和分类(Principal component analysis (PCA), extracting SIFT feature points in images for image recognition and classification)
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一种特征提取方法:结合主元分析(PCA)和核主元分析(KPCA)的前馈神经网络(FNN)(A feature extraction method: the feedforward neural network (FNN) combined with principal component analysis (PCA) and kernel principal component analysis (KPCA))
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基于PCA主成分分析算法和Yale人脸数据库,采用Matlab语言实现的人脸识别程序,整个程序分为预处理、训练、识别、GUI界面控制等几个模块,注释清晰,通俗易懂。(Based on PCA principal component analysis algorithm and Yale face database, the face recognition program is implemented in Matlab language. The whole program is divide
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主成分分析是把原来多个变量划为少数几个综合指标的一种统计分析方法。 从数学角度来看,这是一种降维处理技术。(Principal component analysis (PCA) is a statistical analysis method that divides a number of variables into a few comprehensive indexes.From a mathematical point of view, this is a kind of dimensi
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pca 主成分分析的matlab代码。主成分分析(Principal Component Analysis,PCA), 是一种统计方法。通过正交变换将一组可能存在相关性的变量转换为一组线性不相关的变量,转换后的这组变量叫主成分。(PCA principal component analysis matlab code. Principal component analysis (Principal Component, Analysis, PCA) is a statistical method
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主成分分析法求得主成分回归方程,从而达到降维目的。(Principal component analysis method for obtaining the regression equation of principal components)
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将图像进行主成分分析,并显示第一主成分,方法很好用的(The principal component analysis is performed and the first principal component is displayed.)
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主成分分析法和线性判别分析常用来对原始数据进行简单的数学分析(Principal component analysis and linear discriminant analysis are usually used for simple mathematical analysis of raw data.)
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本程序可以对高维数据进行降维,便于得到主成分进行后续分析。(This program can reduce dimension of high-dimensional data and facilitate principal component analysis for subsequent analysis.)
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主成分分析程序,能够对高维数据降维分析,适用于高维特征降维,大数据分析(The principal component analysis program can analyze dimensionality reduction of high-dimensional data.)
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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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