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一个号的核主成分分析的人脸识别算法，整个程序非常的清楚明了！-A number of kernel principal component analysis for face recognition algorithms, the whole process is very easy to understand!
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有关图形图像的核主成分分析方法 一个很好的例子-Relevant graphic image of kernel principal component analysis method is a good example
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有关图形图像的核主成分分析方法 一个很好的例子-Relevant graphic image of kernel principal component analysis method is a good example
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核主成分分析是一种流行的非线性特征提取方法-
Kernel principal component analysis is a popular nonlinear feature extraction method
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核主成分分析方法是主成分分析的改进算法，其采用非线性方法提取主成分-Kernel principal component analysis method is an improved algorithm of principal component analysis, which uses a nonlinear principal components extracted
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核主成分分析，用于轴承故障，人面识别，水位分布等的数据非线性提取。-Kernel principal component analysis for data bearing failure, human face recognition, water distribution and other non-linear extraction.
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核主分量分析程序，有简单的代码。易学，分享给大家。-Kernel principal component analysis procedure, a simple code. Learn and share with everyone.
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核函数主成分分析，用于数据的特征提取，对于训练样本的降维有较好的效果-Kernel principal component analysis, feature extraction for data, which can effectively reduce the dimension of training samples, the better
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KPCA（核主元分析法）基于MATLAB平台的核主元分析法-KPCA (kernel principal component analysis)
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上传的这个Matlab源代码可以用于主成分分析以及核主成分分析，学者们可以通过此方法实现数据的压缩。-this can be used to have a Principal component analysis and a Kernel Principal component analysis by those research works
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核主成分分析的MATLAB代码，很好用的-Kernel principal component analysis by matlab
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核方法工具箱，包括核典型相关分析KCCA，KPLS等算法的源码和实现。-Kernel Methods Toolbox
KMBOX includes implementations of algorithms such as kernel principal component analysis (KPCA), kernel canonical correlation analysis (KCCA) and kernel recursive least-squares (KRLS).
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是一个关于核主成分分析的MATLAB代码。-It is a MATLAB code on Kernel Principal Component Analysis.
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通过非线性映射将原始空间的向量映射到高维空间，进而提取特征的方法 核主成分分析法-Kernel Principal Component Analysis
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核主成分分析的MATLAB代码，欢迎使用-Kernel Principal Component Analysis MATLAB code, welcomed the use
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核主成分分析KPCA算法，经过核变换将样本映射到线性可分的高维空间，再进行PCA降维。包括训练、测试、识别整个过程-KPCA kernel principal component analysis algorithm through nuclear transformation samples are mapped to linearly separable high-dimensional space, then PCA dimensionality reduction. Including
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mrkpca-故障诊断
主成分分析-核主成分分析的故障诊断-mrkpca-fault diagnosis
Principal component analysis and fault diagnosis of kernel principal component analysis
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核主元分析法，将低维数据，映射到高维空间，进行更精确的非线性划分。-Kernel principal component analysis, the low-dimensional data, mapping to high-dimensional space for more accurate non-linear division.
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KPCA Kernel Principal Component Analysis
- KPCA Kernel Principal Component Analysis
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在具有降维作用的核主成分分析方法基础上增加一个核函数成为新的核主成分分析方法。(On the basis of kernel principal component analysis with dimension reduction, a kernel function is added to be a new kernel principal component analysis method.)
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