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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 minimum distance classifier
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) method, using KL transform and sin
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)