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一个很好的PCA程序。它可用于数据的降维，消噪及特征提取。,A good PCA procedures. It can be used for data dimensionality reduction, de-noising and feature extraction.
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非线性降维方法KPCA 可以应用于高维数据的机器学习-KPCA nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
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核主元分析程序，本人毕业设计程序，用于降维，监测Te过程故障，误诊断率低。-KPCA program, I graduated from the design process for dimension reduction, monitoring Te process failure, error diagnosis rate is low.
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kpca原始程序和小波去噪部分，用于数据降维和特征提取比较实用-kpca part of the original program and wavelet denoising for data dimensionality reduction and feature extraction more practical
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KPCA程序，可用于数据降维，特征提取，用起来比较简单-KPCA procedure can be used for data dimensionality reduction, feature extraction, using relatively simple
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本方法使用sprtool，介绍了Kpca的应用，来进行高位数据降维，介绍三种核函数的应用，并附有结果图。-This method uses sprtool, introduced Kpca applications for high data dimensionality reduction, introduced three nuclear function, together with the results in Fig.
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kpca的算法实现源码，实现了一个数据降维方面的经典算法kpca-kpca the algorithm source code, to achieve a data dimensionality reduction classical algorithm kpca
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kpca的算法实现源码，实现了一个数据降维方面的经典算法kpca-kpca the algorithm source code, to achieve a data dimensionality reduction classical algorithm kpca
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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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核主元分析程序,基于主元分析进行开发编写，可实现核空间数据降维-KPCA program developed to prepare based on principal component analysis, nuclear spatial data dimensionality reduction
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对心电信号QRS波的数据降维，已经把QRS波截取出来-QRS wave of ECG data dimensionality reduction
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此代码是关于流形学习，数据降维，代码中含有的主要方法是PCA，KPCA，MDS，KMDS，Laplacian等等，且代码作了可视化处理，界面效果完美-This code is on the manifold learning, data dimensionality reduction, the main method code is contained in PCA, KPCA, MDS, KMDS, Laplacian, etc., and the code visualization ma
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在matlab上面通过kpca，实现大数据降维算法(Dimensionality reduction algorithm for large data)
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作为多元数据的降维处理方法，有效减小数据的运算量。(As a dimension reduction method for multivariate data, the computation of data is effectively reduced.)
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实现kPCA算法，用于数据降维图像处理等多领域。本程序包可选用多种核函数，且可以直接增添新的数据点，方便快捷。(KPCA algorithm, for data reduction, image processing and many other fields. This package can use a variety of kernel functions, and can directly add new data points, convenient and quick.)
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KPCA算法属于非线性高维数据集降维，算法其实很简单，数据在低维度空间不是线性可分的，但是在高维度空间就可以变成线性可分的了(The KPCA algorithm belongs to the nonlinear high-dimensional data set dimension reduction. The algorithm is very simple. The data is not linearly separable in the low-dimensional space, b
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