搜索资源列表
gabor-pca
- 用gabor结合pca降维实现人脸识别，能得到较好的识别率(face recognition through gabor-pca)
PCA
- PCA主成分分析，提取主特征，降维处理(PCA principal component analysis is used to extract the main features and reduce the dimensionality)
PCA
- 简单的数据降维算法（PCA）举例，构造随机的10维数据，降维成3维的。Sample可替换成用户数据(Examples of simple data reduction algorithms (PCA) are presented)
PCA
- Python实现PCA将数据转化成前K个主成分的伪码大致如下: ''' 减去平均数计算协方差矩阵计算协方差矩阵的特征值和特征向量将特征值从大到小排序保留最大的K个特征(Python PCA data into pseudo code before the K principal components are as follows: the characteristics of 'average minus the covariance
Nonlinear PCA toolbox for MATLAB
- 压缩文件夹中主要包含用于非线性主成分分析的程序(Nonlinear PCA toolbox for MATLAB)
PCA-ICA
- 实现了主元分析（PCA）和独立分量分析（ICA）相关信号处理。非线性降维。(Implements Principal Component Analysis (PCA) and Independent Component Analysis (ICA) correlation signal. Non-linear dimension reduction using kernel PCA.)
gpldecha-e-pca-d542a9b
- PCA是一种非线性降维方法特别适合于概率分布，得到了指数族PCA的POMDPs压缩。(Matlab implementation of E-PCA which is a non-linear dimensionality reduction method particularly suited for probability distributions, see the paper Exponential Family PCA for
pca
- 实现一个简单的PCA降维与重构程序，用到的数据在.txt文件中。还有用于测试的.m文件(To achieve a simple PCA dimension reduction and reconstruction procedures)
PCA
- PCA图像融合效果挺好可以试试.......(PCA fuction image fusion effect is good, you can try)
MNIST-PCA
- 使用PCA算法分析MNIST 手写字符训练样本。 结果分别生成以2、5、10个PCA主成分的重构图像以及10个主成分特征向量的对应图像。(Implement PCA algorithm on MNIST dataset and calculate the class PCA on each digit separately.)
PCA实现特征降维
- pca和_fase_pca实现特征降维，两种算法都有所改进，特别是pca可以根据自己的需要进行相应的改进，代码清晰易懂，希望对你有帮助。(PCA and _fase_pca to achieve feature reduction, the two algorithms have improved, especially PCA can be improved according to their needs, the code is
PCA
- 利用matlab进行pca主成分分析，简单易懂，适合新手(The use of MATLAB PCA, principal component analysis, simple, suitable for beginners)
PCA
- pca降维代码，主要用来给图片进行降维，程序不长，直接用，很方便(PCA Dimension reduction code)
noise level estimation PCA-based
- 利用PCA，提取图像中特征值最小的区域，来估计图像噪声(PCA is used to extract the smallest region of the image feature to estimate the image noise)
PCA-SVM-master
- PCA/SVM算法实现图像分类，分类准确率可到达90%(Image classification by PCA/SVM algorithm)
PCA笔记
- 对PCA的个人理解，有详细的PCA推导过程，代码包括计算协方差，及其特征值特征向量。(For the personal understanding of PCA, there is a detailed process of PCA derivation. The code includes the calculation covariance and its eigenvalue eigenvector.)
PCA
- PCA 算法演示 主要用于数据进行降维处理(PCA suanfa zhuyaoyongyushujujinxingjiangweichuli)
PCA
- 采用INP数据（145*145*200），该数据有16个类别， PCA进行数据降维，然后对降维数据采用kNN分类（k=1）。(Using INP data (145*145*200), the data has 16 categories, PCA carries out data reduction, and then uses kNN classification for dimensionality reduction data
PCA故障诊断步骤
- PCA故障分析程序，主元分析法，故障诊断，TE过程的数据。(PCA fault analysis program, principal component analysis, fault diagnosis, TE process data.)
pca
- 深度学习中的PCA,和白化算法，对深度学习非常有帮助(PCA in deep learning, and albino algorithm, are very helpful for deep learning)