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.
3DFaceRecognitionBasedon3DLBPandKernelDiscriminant
- 二维照片的人脸识别对光照、姿态和化妆等因素很敏感，故提出了一种将三维局部二值模式(3DLBP)和核享，1剐分析(KDA)相结合的三维人脸识剐方法．采用3DLBP描述人脸深度图像的特征，高斯核函数KDA 作为分类器，使用Chi平方统计改进高斯核函数、采用FRGC v2．0中2003春季采集的三维人脸库进行实验．实验结果表明，该 方法在每人2个训练样本时，识别率为91．8％，而PCA和3DLBP的识别率分别为60．4％和78．3％；当每人的训练样本数增至6个时，识别率为98．4％，而PCA和3D
score
- 对获得的512维小波特征进行了PCA降维处理，将特征空间降至100维，并采用前面得出的最佳系数针对多项式核函数和高斯径向基核函数-On access to the 512-dimensional wavelet features to reduce the dimension of the PCA, will feature space to 100 dimensions, and using the best coefficients obtained for the previous pol
KPCA
- KPCA是一种基于核的主要成分分析，是一种由线性到非线性之间的桥梁。通过非线性函数把输入空间映射到高维空间，在特征空间中间型数据处理，引入核函数，把非线性变换后的特征空间内积运算转换为原始空间的核函数计算。 基本思想是通过某种隐士方法将输入空间映射到某个高维空间（特征空间），并在特征空间实现PCA。对该算法进行了详细的说明-KPCA is a kernel-based principal components analysis, is a bridge between the linear
pcaaKPCA
- 主分量分析 和 核主分量分析的 原理简介，主分量分析（PCA）用于对信号进行特征提取和降维-Introduction of the principle of the principal component analysis and kernel principal component analysis, principal component analysis (PCA) for feature extraction and dimensionality reduction of signal
NLKPCA
- 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
Neural-Network
- This folder contains the following sub-folders which are essential in our project: 1.Raw Data All the raw data collected from Flagstaff hill, CMU Athletic Field, and Railroad on Neville St. 2.Filter Filter to rule out signal of Channel
pcakernel
- the new approch pca with kernel this is a code
kpca1
- 在核空间中进行pca，简称kpca的源码-kernel principle component analysis
13FaceRec
- 人脸特征提取与识别matlab程序，主要提取了PCA特征、SVM分类和核方法分类等，代码可以直接使用-Face recognition based on PCA features and Kernel methods, which is used in pattern extraction.
KPCA
- 另尝试编写的一个代码如下，实现对3类数据特征空间的聚类，如下图，红、绿、蓝三种颜色分别表示3类数据，经过rbf核映射到新的空间后，分别聚成了3类-This technique takes advantage of the kernel trick that can be used in PCA. This is a tutorial only and is slow for large data sets. In line 30 the kernel can be changed. Any Ke
KPCA
- 核主成分分析KPCA算法，经过核变换将样本映射到线性可分的高维空间，再进行PCA降维。包括训练、测试、识别整个过程-KPCA kernel principal component analysis algorithm through nuclear transformation samples are mapped to linearly separable high-dimensional space, then PCA dimensionality reduction. Including
SVM-KMExample
- examples of SVM, PCA , MultiSVM, Feature extraction, kernel function
function_kpca
- 使用核函数，在matlab环境下实现非线性主成分分析(Using kernel function to realize nonlinear principal component analysis in Matlab environment.)
KECA_Journal_Article
- 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
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