搜索资源列表
Pca-extraction
- pca进行特征提取源码,用matlab语言编写,pca即主成分分析-pca source for feature extraction using Matlab language, pca that Principal Component Analysis
pca-svm
- 本程序用于对训练样本提取独立主元,作为样本特征,并送入SVM分类器中训练图像的预处理中不取对数,也无须做幅度归一,由ICA的应用条件决定的。预处理后的图像以向量的形式按行排列
ResearchandRealizationinFaceDetection
- 一篇有关人脸检测的博士论文,提出了一种改进的pca特征提取方法,综合运用小波和离散余弦法对的支持向量机对人脸进行检测
Subpattern-based_principal___component_analysis.zi
- 子模式主成分分析首先对原始图像分块,然后对相同位置的子图像分别建立子图像集,在每一个子图像集内使用PCA方法提取特征,建立子空间。对待识别图像,经相同分块后,分别将子图像向对应的子空间投影,提取特征。最后根据最近邻原则进行分类。-Sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the
pattern-recognition-simulation
- 用mushrooms数据对模式识别课程讲述的各种模式分类方法[线性分类,Bayesian分类,Parzen窗,KNN]和特征选择和降维方法[PCA,LDA]进行了模拟,并给出了各类分类方法的结果,-It s the simulations about linear classification ,Bayesian ,Parzen and KNN of pattern recognition .And ,It gives the results.
pca
- PCA主元分析后用神经网络预测,A/S含量,PCA算法实现,与神经网络-PCA principal component analysis using neural network prediction, A/S content, PCA algorithm, and neural network
PCA
- PCA矩阵的算法,能得到图像的特征向量,很好的!-PCA matrix algorithm, very good!
fum
- 标准化后进行PCA特征提取,然后聚类分类-After standardized PCA feature extraction, clustering and classification
PCA
- 主要介绍主分量分析,怎样提取主要特征来重构原始信号-Introduces the principal component analysis, how to extract the main features to reconstruct the original signal
zhongshu
- 人脸识别 求特征向量 均值脸 pca 特征值 支持向量包 各种M文件 支持向量机的应用发展等-Mean face recognition pca eigenvector eigenvalue vector covering all M documents the application of support vector machine development
UPCAA_faceRees
- 用Visual C++实现的人脸识别Demo程序源码。Adabooost实现人脸检测,PCA特征提取。 -Face Recognition Demo Visual C++ achieve program source code. Adabooost face detection, PCA feature extraction.
PCA_basedPFacePRecognition
- 非常不错的人脸识别程序,基于PCA特征,附带测试和训练样本,编译通过且附带注释。-Very good face recognition program, based on the PCA feature included with the test and training samples, compiled by and annotated.
pca
- 主成分分析法实现了对于多维特征的优化,共两个文件,希望对大家有帮助-failed to translate
PCA
- 特征提取算法(pca),用于数据的特征提取。-feature extraction
CC
- pcA,特征提取以及分类,可以运行.欢迎提意见,-pcA, feature extraction and classification, you can run
PCA
- (主成分分析)Jacobi法求解对称矩阵的特征值及特征向量-(PCA)Finding the eigenvalues and eigenvectors of symmetric matrices, applying Jacobi s method
pca
- pca是主成分分析,提取特征,对数据进行降维处理(PCA is principal component analysis, which extracts features and processes the data in reduced dimension)
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 matrix to calculate the covari
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 clear and easy to understand,