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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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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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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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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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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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