搜索资源列表
kpca
- KPCA的提出者亲自写的程序。是一份很值得收藏的经典代码。-KPCA the author himself procedures. It is a very worthwhile collection of classic code.
sime-kpca
- 基于半监督的核主元分析matlab代码，基于半监督核主元分析matlab代码-Based on the semi-supervised KPCA Matlab code, Based on the semi-supervised KPCA Matlab code
KPCA
- 在ORL或Yale标准人脸数据库上完成模式识别任务。用PCA与基于核的PCA（KPCA）方法完成人脸图像的重构与识别试验. -Or Yale in the ORL face database, complete the standard pattern recognition tasks. With the PCA and kernel-based PCA (KPCA) method to complete the reconstruction of face image and reco
KPCA
- matlab 源码 KPCA/核PCA 可用于人脸识别-matlab source KPCA/nuclear PCA for Face Recognition
kpca
- 运用KPCA方法在ORL人脸库上进行人脸识别，分类器为最近邻分类器。-KPCA method using ORL face database for face recognition, classification for the nearest neighbor classifier.
KPCA
- 用于人脸识别特征提取的KPCA算法，很好的程序，有问题大家交流-KPCA for face recognition feature extraction algorithm, a very good program, there are problems we share
KPCA
- 关于KPCA的详细算法说明 并附有相关例子-Detailed descr iption of KPCA algorithm on together with relevant examples
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 framework of its essence is the KPCA+ LGE within the framework of linear dimensionality reduc
mean-K-KPCA
- 通过核 K- 均值聚类的方法对语音帧进行聚类 , 由于聚类的中心能够很好地代表类内的特征, 用中心样本帧取代该类, 减少了核矩阵的维数, 然后再采用稀疏 KPCA方法对核矩阵进行特征提取。-Through the nuclear K-means clustering method for clustering of speech frames, the cluster center can be a good representative of the class characteristics
kpca
- KPCA的matlab代码，经过验证，可以用的代码-matlab code for KPCA,it can be used
KPCA和PCA比较分析
- 具体分析了KPCA的核函数选择，参数选择对累计方差贡献率的影响
KPCA
- 基于KPCA的故障检测，包里包含了测试数据和训练数据，可以直接运行(Based on KPCA fault detection, the package contains test data and training data that can be run directly)
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, convenient and quick.)
KPCA故障检测程序（代码已优化）
- 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
KPCA实现
- 有一个讲解kpca的个PPT，和一个用MATLAB实现的kpca的程序(There is a PPT explaining KPCA and a KPCA program implemented by MATLAB.)
KPCA故障检测
- 基于KPCA的故障诊断matlab代码。包括t^2统计量和q统计量(Fault Diagnosis Matlab Code Based on KPCA)
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
核函数主成分分析KPCA
- 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
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.)