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维数约简工具箱源代码,包括PCA、LLE等学习算法,可用于模式识别、数据挖掘和统计分析等。-dimension reduction toolkit source code, including the PCA, LLE and other learning algorithms can be used for pattern recognition, data mining and statistical analysis.
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数据的可视化,rar文件里面包含了可视化的效果,可以画出4-D的图形,颜色作为第四维,更好的呈现数据的空间结构,可以用PCA等做降维的可视化展现-Data visualization, rar file which contains a visual effect, you can draw a 4-D graphics, color as the fourth dimension, the better the spatial structure of the present data, yo
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主成分分析的主要目的是希望用较少的变量去解释原来资料中的大部分变异,将我们手中许多相关性很高的变量转化成彼此相互独立或不相关的变量。通常是选出比原始变量个数少,能解释大部分资料中的变异的几个新变量,即所谓主成分,并用以解释资料的综合性指标。由此可见,主成分分析实际上是一种降维方法。-The main purpose of PCA is to use fewer variables to explain most of the variation of the original data will
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This tutorial is designed to give the reader an understanding of Principal Components
Analysis (PCA). PCA is a useful statistical technique that has found application in
fields such as face recognition and image compression, and is a common techn
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PCA的步骤:
1 先将数据中心化;
2 求得的协方差矩阵;
3 求出协方差矩阵的特征值与特征向量;
4 将特征值与特征向量进行排序;
5 根据要降维的维数d’,求得要降维的投影方向;
6 求出降维后的数据;
-PCA steps: 1 of the first data center 2 covariance matrix obtained 3 obtained covariance matrix eigenvalues and eigenvectors 4
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关于SVM数据预处理的函数,降低维数的PCA函数-The function of data pretreatment on SVM, reduce dimension of PCA function
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This tutorial is designed to give the reader an understanding of Principal Components
Analysis (PCA). PCA is a useful statistical technique that has found application in
fields such as face recognition and image compression, and is a common techn
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利用PCA提取人脸特征,。利用PCA技术降低维数去除了原始原始数据之间的关联性。-Using PCA face feature extraction,. Using PCA technology to reduce dimension in addition to the original connection between the original data.
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图像的特征提取,典型案例是基于PCA技术的人脸数据集得降维处理-Image feature extraction, a typical case is based on PCA face data set to reduce the dimension of
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最新最强MATLAB降维工具箱,可用于人脸识别,模式识别,机器学习,数据挖掘,图像处理等领域,里面包含的算法有PCA,LDA,KPCA,KLDA,Laplacian,LPP,MDS,NPE,SPE,LLC,CFA,MCML,LM-The latest and greatest dimension reduction MATLAB toolbox can be used for face recognition, pattern recognition, machine learning, dat
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以著名的wine数据作为实验样本。包括k-NN算法,交叉验证,PCA降维等。-With the famous wine data as experimental samples.K- NN algorithm, cross validation, PCA dimension reduction, etc.
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主成分分析,对多特征数据进行主成分分析,降低样本的维度,实现分类前的预处理。-Principal component analysis, principal component analysis was carried out on the characteristic data, reduce the dimension of sample pretreatment before implement classification.
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主成分分析(principal component analysis,PCA)是一种将高维数据投影到低维
数据的线性变换方法,这一方法的目的是寻找在最小均方意义下最能代表原始数据特征
的投影方向,用这些方向矢量表示数据。-Principal component analysis (PCA) is a kind of high dimensional data to the low dimension.The objective of this method is to find the
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利用pca进行数据降维,代码简单,比较容易理解-Using pca to data dimension reduction, the code is simple, easy to understand
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PCA算法:通过将高维矩阵降维来压缩文件或是查询数据-PCA algorithm: by reducing the dimension of the high-dimensional matrix to compress the file or query data
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高光谱遥感与传统的单波段、多光谱数据相比,波段量大量增加、波段宽度极大降低,对地面目标的光谱特性的测度更加细致,然而波段的增多必然导致数据量急剧增加、计算量增大、信息冗余增加以及统计参数的估计偏差增大。因此,对高光谱数据进行降维处理具有重要意义。一方面,降维能够使图像远离噪声,提高图像数据质量;另一方面,能够去除图像中的无价值波段,减少波段数,从而降低计算量,提高运算效率。主成分分析是常用的高光谱数据降维处理方法之一。(Compared with the single band, hypersp
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主成分分析是多元统计分析中用来分析数据的一种方法,它是用一种较少数量的特征对样本进行描述以达到降低特征空间维数的方法(Principal component analysis is a method of data used in multivariate statistical analysis, it is describing the samples with characteristics of a small number of methods to reduce the dimens
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Matlab 平台使用2DPCA对二维图像数据进行降低图片的维度。提高图像处理速度。(The Matlab platform uses 2DPCA to reduce the dimension of the image data for two-dimensional image data. Improve the speed of image processing.)
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首先对minist数据集进行pca降维,然后对降维后的数据进行KNN分类(First, the Minist data set is reduced by PCA, and then the data of the reduced dimension is classified by KNN)
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PCA降维算法,本程序已经调好,可以直接跑数据(PCA dimension reduction algorithm, this program has been adjusted, you can run data directly)
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