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pdf principal component analysis kernel pca
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基于核函数的广义判别算法，使用matlab语言编程-Principal component analysis based on kernel function algorithm, using matlab language programming
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Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensiona
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Matlab针对各种数据预处理的降维方法，源码集合。-Currently, the Matlab Toolbox for Dimensionality Reduction contains the following techniques:
Principal Component Analysis (PCA)
Probabilistic PCA
Factor Analysis (FA)
Sammon mapping
Lin
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这是外国人实现的非线性主成份分析，可下载相应的文章，可用来降维！-Applies the kernel method to unsupervised algorithms as for instance Principal Component Analysis. This gives a principled and efficient approach to nonlinear PCA
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A new method for performing a nonlinear form of Principal
Component Analysis proposed. By the use of integral operator kernel
functions, one can eciently compute principal components in high{
dimensional feature spaces, related to input space
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核主元分析程序，利用核函数将非线性数据映射到线性空间，再利用主元分析提取特征数据。-KPCA procedure uses the kernel nonlinear data mapped into linear space, and then using principal component analysis to extract feature data.
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基于核方法的各种算法，主成分分析，fish方法等-A variety of algorithms based on kernel method, principal component analysis, fish method
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this principle component analysis(PCA) that is used for linear dimensionality reduction and feature extraction as well as the nonlinear form of PCA which is KPCA and how PCA is used for Active shape models -this is principle component analysis(PCA) t
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本程序使用MATLAAB R2014a 编写，基于PCA_SVM的人脸识别程序。程序包括主成份分析、SVM核函数，并附带了人脸库，使之能够直接调用人脸库图像进行人脸识别-The program uses MATLAAB R2014a written procedure based on recognition of PCA_SVM. Program includes principal component analysis, SVM kernel function, and comes face
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核独立主元分析(KICA算法)在模式识别、过程监测、故障诊断等不同领域的应用中都表现了很好的性能。-Kernel independent principal component analysis (KICA algorithm) has shown good performance in pattern recognition, process monitoring, fault diagnosis and other fields.
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AN MR BRAIN IMAGES CLASSIFIER VIA PRINCIPAL
COMPONENT ANALYSIS AND KERNEL SUPPORT
VECTOR MACHINE
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核主成分分析方法，过程非常详细，可用于分类和降维(The kernel principal component analysis method is very detailed and can be used for classification and dimensionality reduction)
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Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010.(We introduce kernel entropy component analysis (kernel ECA) as a new method fo
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一种特征提取方法：结合主元分析(PCA)和核主元分析（KPCA）的前馈神经网络（FNN）(A feature extraction method: the feedforward neural network (FNN) combined with principal component analysis (PCA) and kernel principal component analysis (KPCA))
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基于核主元分析（KPCA）的工业过程故障检测，代码已优化，运行效率高，有详细的注释，附有训练数据和测试数据。(Achieves fault detection of industrial processes based on Kernel Principal Component Analysis (KPCA); the code has been optimized for high operational efficiency; detailed notes are attached with
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Robert Jenssen 撰写论文原文(We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated
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在多元统计领域中，核函数主成分分析(kernel principal component analysis, kernel PCA)是利用核函数方法技术对主成分分析(PCA)的扩展。使用核函数使原PCA的线性操作是在一个复制的内核希尔伯特空间中执行的。 KPCA的运算步骤势在PCA之前首先对数据进行kernel变换 ，再求相关系数矩阵。(In the field of multivariate statistics, kernel principal component analysis (ke
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这个是KPCA核主成分分析的代码，好用，里面也带有范例(This is the KPCA kernel principal component analysis code, which is easy to use and also contains examples.)
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主要功能有：
（1）训练数据和测试数据的非线性主元提取（降维、特征提取）
（2）SPE和T2统计量及其控制限的计算
（3）故障检测
KPCA的建模过程（故障检测）：
（1）获取训练数据（工业过程数据需要进行标准化处理）
（2）计算核矩阵
（3）核矩阵中心化
（4）特征值分解
（5）特征向量的标准化处理
（6）主元个数的选取
（7）计算非线性主成分（即降维结果或者特征提取结果）
（8）SPE和T2统计量的控制限计算
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