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This toolbox is an educational and recreative toolbox around recent ideas in the field of dimension reduction.
* PCA : classical Principal Componnent Analysis (linear projection).
* Nonlinear dimensionality reduction by locally linear embedding
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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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pca算法已经广泛应用于各方面，当提取的图像特征维度比较高时，为了简化计算量以及储存空间，需要对这些高维数据进行一定程度上的降维，并尽量保证数据的不失真。-pca algorithm has been widely used in various areas, when the extracted image feature dimension is relatively high, in order to simplify the calculation and storage space n
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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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实现高维数据向低维数据的转变，降低维数希望群主采纳！-To realize the change of high dimensional data to low dimensional data, reduce the dimension of hope group of main adopted!
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对PTS文件中进行数据的读取，然后进行降维操作，得到结果-Read data of PTS file, and then for dimension reduction operation, and get the result
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人脸搜索简单实现，使用主成分分析算法(PCA)，依赖opencv，对若干张人脸图片进行PCA降维处理，然后将输入人脸图片与降维后的数据做比较，根据权重输出结果，权重越大则人脸越相似-Simple face search, using the principal component analysis (PCA) algorithm, opencv, for a number of face images for PCA dimension reduction, and then the input
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基于主元分析的异常检测和故障诊断，用于对具有高度线性相关的测量数据进行分析和处理，其最终实现高维空间降维的目的。-Anomaly detection based on principal component analysis and fault diagnosis, used for highly linear correlation measurement data analysis and processing, its ultimate achieve the goal of higher
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PCA降维算法应用大数据挖掘中，在大数据环境下实现数据的降维，可按需要自行修改代码-PCA dimensionality reduction algorithm in data mining, in the big data environment for data dimension reduction, according to need to modify the code itself
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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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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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应用于数据降维的一种MATLAB程序，可以实现从高维到低维的降解(A matlab program applied to data dimensionality reduction can realize the degradation from high dimension to low dimension)
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