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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