搜索资源列表
KL2
- 人脸识别：利用奇异值分解和KL变换的投影，是很有价值的一篇文章-Face Recognition : The Singular Value Decomposition and KL transform projection, it is valuable to an article
KL2
- 人脸识别：利用奇异值分解和KL变换的投影，是很有价值的一篇文章-Face Recognition : The Singular Value Decomposition and KL transform projection, it is valuable to an article
chengxu
- 这是基于PCA的人脸识别，用MATLAB编写，包含了K-L变换，奇异值分解等方法，且采用了最小距离分类器-This is based on the PCA face recognition, using MATLAB to prepare, including the KL transform, singular value decomposition and other methods, and the use of the mini
zuijinlinfenlei
- 我们使用MATLAB软件实现了人脸识别并统计其识别率。本实验采用PCA（主成分分析）方法，利用K-L变换和奇异值分解原理实现。并分别采用最近邻法分类器得出它们的成功率。-We use face recognition software and the MATLAB Statistics recognition rate. The present study, PCA (principal component analysis) meth
KL_SVD_face_recognition
- PCA主成分分析，采用KL投影和SVD分解提取人脸特征向量，最后采用最近邻判别法计算识别率。-Face recognition based on PCA. KL projection and SVD are used to extract face eigenvectors. Recognition rate is calculated by k nearest neighbors(KNN) method.
3.15
- 对emd分解进行改进，通过kl散度和相关系数来改进这个分解排除虚假分量-Improve the decomposition of emd, improve the decomposition by kl divergence and correlation coefficient to eliminate false components
KLT
- 本程序实现了利用KL变换进行特征分解，并进行降维重建，示例图片在文件中给出。(This program realizes feature decomposition using KL transform and dimensionality reduction reconstruction. The example pictures are given in the file.)
KL
- 用于实现随机过程的KL分解，附数据文件，可直接运行(it is used for KL decompsition of random process)