搜索资源列表
drtoolbox
- 降维工具箱，包含主元分析（PCA）,核主元分析（KPCA）等。-Dimensionality reduction kit, including principal component analysis (PCA), Kernel Principal Component Analysis (KPCA) and so on.
kpca
- KPCA降维算法的实现函数，matlab的函数-KPCA dimensionality reduction algorithm to achieve the function, matlab function
KPCA
- KPCA主要在图像去噪声方面有应用。此外还可以进行特征提取，降维使用.-KPCA major noise in the image to have the application. You can also feature extraction using dimension reduction.
Fault7_KPCA0
- 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
kpca
- 核主分量分析matlab程序.对train进行基于高斯径向基kpca降维，x行数目为样本数，列数目为特征数,并用test进行测试-program for KPCA in matlab.
kpca
- 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
特征降维
- 各种降维的方法，KPCA,KLDA,KLPP，应有尽有
kpca
- kpca降维算法，可用于高维数据的预处理，里面有详尽的注释-kpca u964D u7EF4 u7B97 u6CD5 uFF0C u53EF u7528 u4E8E u9AD8 u7EF4 u6570 u636E u7684 u9884 u5904 u7406 uFF0C u91CC u9762 u6709 u8BE6 u5C3D u7684 u6CE8 u91CA
kpca1
- 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.)
KPCA
- 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)
kpca
- 实现数据语音数据的降维，去除冗余 提高预测的精度(To reduce the dimension of data speech data, to eliminate redundancy and improve the prediction accuracy)
KPCA
- 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
KPCA-故障检测
- 内附有对应的数据集，直接测试即可。利用KPCA进行降维。(With data sets, direct testing is enough.)
LLTSA降维
- 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.)