搜索资源列表
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_toy
- 基于kernel pca的非线性降维算法，原文发表于神经计算杂志上，有兴趣者可以先看论文。-PCA-based kernel of nonlinear reduced dimension algorithm, the original published in the Journal of neural computation, those interested can read papers.
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. In addition can also be used for feature extraction, dimensionality reduction using
KLPP
- 核lpp（局部保持映射）的降维方法。跟Xiaofei He的论文配套-Nuclear lpp (partial maintain mapping) methods of dimensionality reduction. Xiaofei He told the paper supporting
KPCA
- 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.
kpca081223
- 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
kpcaprogram
- 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.
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.
KPCA-LEG
- 从理论上证明了KGE框架内的各种核算法其实质是KPCA+LGE框架内的线性降维算法，并且基于所给出的理论框架提出了一种综合利用零空间和非零空间 鉴别信息的组合方法．-Theoretically proved that the KGE various accounting method within the fr a mework of its essence is the KPCA+ LGE within the fr a mew
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-ELM
- 基于Stprtool 工具箱进行KPCA降维，然后运行ELMS算法。-Stprtool calculation based on KPCA and then ELM algorithm prediction.
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
kPCA
- 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
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)
PCA,KPCA完整程序
- 降维，用作聚类算法使用。具有很好效果，可以用作图像去噪(Dimensionality reduction is used as a clustering algorithm. It has good effect and can be used for image denoising.)
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 i
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.)