搜索资源列表
PCA
- 数据挖掘中很重要的主成分分析(PCA)算法-Principal Component Algorithm in Data Mining
Experimental
- 实验主成分分析，主要分析影响实验结果的主要因素-Experimental principal component analysis，Experimental principal component analysis
Face
- 基于主成分分析方法的人脸识别系统 自带训练图片以及测试图片-Training comes with pictures and test image recognition system based on principal component analysis method
PCARegress
- 主成分分析法中(PCA)的主成分回归，相比最小二乘回归具有更小的均方误差。本程序用于学习交流。-Principal component analysis method (PCA) of the principal component regression. Compared to the least squares regression, it s a smaller mean square error. This procedure just for learning.
PCA
- 例1为从相关系数矩阵出发进行主成分分析，例2为从excel中读取数据并进行主成分分析。-Example 1 is a departure the correlation coefficient matrix principal component analysis, for example 2 to read data excel and principal component analysis.
pca_example
- 主成分分析代码实现，可输出主成分系数，主成分得分，特征根，贡献率，累计贡献率-pca example
KernelICA
- KICA是对独立主成分分析的核函数化，具有更好的鲁棒性。-KICA is the kernel function of the independent principal components analysis and has better robustness.
KPCAmatlab
- 核主成分分析方法kpca在matlab中的实现，包含参数优化等-KPCA in matlab
C-matlabfastica
- C版的主成分分析代码，可作为matlab工具箱-C version of the principal component analysis code, can be used as the matlab toolbox
pca
- pca主成分分析，人脸识别,200图遍历-pca principal component analysis, face recognition
PCA
- 主成分分析，对多特征数据进行主成分分析，降低样本的维度，实现分类前的预处理。-Principal component analysis, principal component analysis was carried out on the characteristic data, reduce the dimension of sample pretreatment before implement classification.
pca
- pca+src主成分分析是一个定量的严格的可以起到简化作用的方法。它产生一组叫做主成分的新变量，每一个主成分是原始变量的线性组合。-pca+ src principal component analysis is a quantitative strict action can play a simplified approach. It generates a new set of variables, called principal components, each principal
pca
- PCA ( principal component analysis ) 主成分分析
GaborP2dpca
- 人脸识别中主成分分析法和gabor滤波器相结合。Gabor + 2dpca-Image Processing，Gabor plus 2dpca
MAIN
- 主成分分析，输入各指标的判断矩阵，得出指标的权重系数，并进行CR一致性检验-Principal component analysis
pca
- 人脸识别的代码，主成分分析法，简单实用，yale数据库-face recognition
Kmeans-and-PCA
- K—mean分类和主成分分析法的应用实例，很明晰的讲解实例，非常适合算法应用的学习-Application examples Kmean classification and principal component analysis, it is clear to explain instance, very suitable learning algorithm
bh_tsne.tar
- 本代码实现TSNE降维，首先利用PCA进行主成分分析，选取何时的特征再降维-dimension reduction for TSNE，we first use Principal Component Analysis to dimension reduction.
pca-daima
- 近红外光谱主成分分析，适用于各类光谱定性和定量计算使用-Near Infrared Spectroscopy principal component analysis, applicable to all types of spectrum use qualitative and quantitative calculation
factor
- 给定一个矩阵，求出其主成分个数，方差贡献率等。并用SPSS检验。-Given a matrix, find its number of principal components, variance contribution rate. Test with SPSS.