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程序说明:y = pca(mixedsig),程序中mixedsig为 n*T 阶混合数据矩阵,n为信号个数,T为采样点数。y为 m*T 阶主分量矩阵。n是维数,T是样本数。-Procedure Note: y = pca (mixedsig), the program mixedsig for the n* T-order mixed data matrix, n is the signal number, T the sampling points. y for m* T-order pri
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用pca 和 lda 实现数据的降维,加快机器的特征提取的速度。-Pca and lda of data with dimension reduction, feature extraction to speed up the speed of the machine.
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This an efficient implementation of PCA, which use smaller dimension of the data matrix to compute the eigenvectors-This is an efficient implementation of PCA, which use smaller dimension of the data matrix to compute the eigenvectors
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PCA技术的一大好处是对数据进行降维的处理。我们可以对新求出的“主元”向量的重要性进行排序,根据需要取前面最重要的部分,将后面的维数省去,可以达到降维从而简化模型或是对数据进行压缩的效果。同时最大程度的保持了原有数据的信息。-A major advantage of PCA technology is reduce the dimension of the data processing. We can calculate the new " principal component&qu
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模式识别作业-完全自编仿真程序。先用PCA对IRIS数据集进行降维,然后用最小错误法对降维的数据进行分类。压缩包中既包括matlab源代码,又有自己写的报告,还有.MAT格式的IRIS数据集用作程序调用。程序有详细注释,很容易懂。最后结果输出到txt文件中。-Pattern recognition operations- completely self simulation program. First on the IRIS data set with PCA dimension reduct
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实现lle和pca的matlab编程,主要目的是比较lle和pca这两个高维数据降维方法的性能,各有所长。-Use matlab to realise lle and pca ,the purpose is to compare lle and pca,which reduce high dimension data to low data dimension,and each has advantage!
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PCA (Principal component analysis) is used to redraw dimension of data
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pca(主成分分析)matlab实现,可以有效的提取数据的主元成分,降低数据矩阵的维数。-pca (Principal Component Analysis) Matlab implementation, can effectively extract data in the main ingredients, reduce the dimension of the data matrix.
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PCA是基于原始数据空间,通过构造一组新的潜隐变量来降低原始数据空间的维数。-PCA is based on the original data space, by constructing a new set of latent variables to reduce the dimension of the original data space.
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in this program I have deduced dimension of iris data with PCA
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Fisherface方法的实现是在PCA数据重构的基础上完成的,首先利用PCA将高维数据投影到低维特征脸子空间,然后再在这个低维特征脸子空间上用LDA特征提取方法得到Fisherface。程序中使用参数寻优的方法来寻找最佳投影维数,以达到比较理想的识别效果。-The Fisherface method implemented in the PCA data reconstruction based on the completion of the first use of PCA project
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this article describes the ideas behind of principal component analysis and uses that to reduce the dimension of a data space
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这是关于PCA主成分分析降维,文件夹里有.mat格式的数据文件,里面数据可供测试PCA降维使用。-This is about PCA principal component analysis dimension reduction, folder. Mat data file format, data available for test use PCA dimensionality reduction.
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可以很好的实现流形学习算法中的线性降维算法PCA数据降维。-Can well realize the manifold learning algorithm of linear dimension reduction algorithm of PCA data dimension reduction.
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主成分分析(PCA)的MATLAB源程序,数据降维处理-Principal component analysis (PCA) of MATLAB source, data dimension reduction process.
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再用主成分分析方法进行信号噪声去除。文件中包含三个子函数:1.Normalize_InputData,规则化输入数据;2.PCA_Reduce_Dimension,实现数据降维处理;3.PCA_Filter_Noise,实现噪声去除(the principal component analysis method for signal noise removal. The file contains three sub-functions: 1.Normalize_InputData, regul
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这是吴恩达在course公开课上讲的数据降维的作业的代码,主要是应用PCA对数据降维(This is Wu Enda in the course open class lectures on data dimension reduction operations code, mainly using PCA for data dimensionality reduction)
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通过Python实现了PCA数据降维的方法(The method of reducing the dimension of PCA data through Python)
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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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pca、svd、mds
数据降维,用来对数据进行预处理
新人学习,叨扰(pca data dimension reduction)
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