搜索资源列表
PCA-SIFT
- 介绍基于PCA的SIFT算法实现的文章,文章是英文的-Introduced the SIFT algorithm based on PCA' s article, the article is in English
mani
- 运行在MATLAB上,可以实现PCA,LLE,LE,HLLE,LTSA等算法-Run in MATLAB, you can achieve the PCA, LLE, LE, HLLE, LTSA and other algorithms
Face-recognition--on-a-DSP
- 本文介绍了 DSP6711的硬件特性 分析了人脸检测、 识别的原理及算法的选型 运用基于 DCT变换域的 LDA的特征提取方法 ,实现了人脸的自动识别。在 Yale人脸库上的实验结果表明本算法识别率要比直接用 PCA进行特征提取的方 法要好-This article describes the DSP6711 hardware features analysis of face detection, recognition of the principle and algorithm se
pca
- pca算法的matlab实现,进行数据的降维处理-Pca algorithm matlab, the data dimension reduction processing
PCA
- 采用数字图像主分量分析(PCA)算法实现人脸识别(身份认证或分类)-Principal components using digital image analysis (PCA) algorithm for face recognition (identity or category)
A0091451_PR_PJ2
- PCA、NMF、LDA、GMM这些算法的matlab实现 解压后文件夹中包含需识别的库、课题要求、课题报告和实现的代码。-PCA,NMF,LDA,GMA these four algorithms Matlab code are referred here. After being decompressed,the file shows the image library to be dealt with,the requirement of the project,the repor
PCA-and-MatlabCode
- 主成分分析法实现特征选择,里面有非常详细的算法介绍,还有一个现实实例介绍,非常具象,里面贴有实现算法的matlab代码-Principal component analysis (PCA)to achieve feature selection , which has a very detailed descr iption of algorithms , there is a real example to illustrate , very concrete , which is affix
PCA
- PCA算法描述,pca实现代码,作为图形图像处理有很重要的作用-PCA descr iption of the algorithm
pca
- 主成分分析最经典的入门文档,还有前序知识的文档——协方差矩阵,以及实现代码。-The principal component analysis the most classic of introductory document, and knowledge of the preface to the documentation-covariance matrix, and realize the code.
pca-transformation
- matlab实现高光谱图像主成分变换,以突出图像的重点-pca transformation
pca
- 模式识别里面的经典PCA算法,用OpenCV视觉开发库实现的。该算法主要用于训练分类器,然后来对人脸来进行识别。-PCA algorithm developed with OpenCV
new-pca
- 这是个改进的PCA主元分析程序 可以实现数据降维和压缩-This is a PCA improved principal component analysis program can reduce the dimensionality of data compression
PCA-1-matlab
- Matlab实现主成分分析的一种方法,希望对初学者有帮助-Matlab as a principal component analysis method, for beginners
pca2
- PCA 主成分分析 特征抽取 特征降维 matlab实现-PCA principal component analysis feature extraction dimension reduction
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
pca
- pca(主成分分析)matlab实现,可以有效的提取数据的主元成分,降低数据矩阵的维数。-pca (Principal Component Analysis) Matlab implementation, can effectively extract data in the main ingredients, reduce the dimension of the data matrix.
PCA-matlab
- PCA人脸识别的MATLAB实现,包括了ORL人脸数据库。-PCA face recognition in MATLAB, including the ORL face database.
PCA
- 利用单片机的PCA功能,实现对信号频率的测量,精度较高-PCA frequency measurement
PCA
- 主元分析中对主元的个数确定目前没有非常好的办法,这里提供一个比较方便实现的matlab程序,利用累计方差贡献率确定主元个数的matlab程序-Principal component analysis of main elements to determine there is no good way, here to provide a more convenient realization of the matlab program, using the cumulative variance
PCA
- PCA算法实现,对一组数据进行主成分分析。文件中提供的CMyPCA.cpp文件实现了PCA算法,并在工程文件中通过举例说明了算法的使用方法。-PCA algorithm, principal component analysis of a set of data. Provided in the document CMyPCA.cpp files PCA algorithm, and illustrates the use of the algorithm adopted in the proj