搜索资源列表
BCI_deneme_LDA
- LDA training codes related to BCI
FisherFace_New
- PCA和LDA算法的融合，适用于人脸识别中-Fusion of PCA and LDA Algorithm for Face Recognition
LDA_11_16
- 自己经过修改的LDA算法源程序，证明可用-Own modified LDA algorithm source code, proven available
ldaknn
- matlab code for lda and knn based classifier
mhaghighat-gda-87032da
- GDA是LDA算法的核变换版本，简称广义线性判别，能将高维数据投影到低维空间-GDA is the nuclear transformation version of LDA algorithm, referred to as generalized linear discriminant, can be high-dimensional data projection to low-dimensional space
9927429LDA
- 线性判别式分析，可以用来对数据进行分类或数据降维（不同于PCA）(Linear discriminant analysis)
FisherFace_New
- LDA算法 将带上标签的数据（点），通过投影的方法，投影到维度更低的空间中，使得投影后的点，会形成按类别区分，一簇一簇的情况，相同类别的点，将会在投影后的空间中更接近。(Linear Discriminant Analysis)
2DLDA PK LDA for feature extraction
- 2D线性判别分析工具箱，该方法为Pattern recognition letters关于2DLDA的源码(2D linear discriminant analysis toolbox, which is the source code for Pattern recognition letters on 2DLDA)
kpmf
- Matlab code for bayesian LDA.
statistics_kmeans
- K-means算法是一种硬聚类算法，根据数据到聚类中心的某种距离来作为判别该数据所属类别。K-means算法以距离作为相似度测度。(kmeans uses the k-means++ algorithm for centroid initialization and squared Euclidean distance by default. It is good practice to search for lower, local minima by setting the 'Replica
MATLAB人脸识别
- MATLAB人脸识别（PCA,LDA,KPCA,BP，可视化界面，摄像头）(Matlab face recognition (PCA, LDA, KPCA, BP, visual interface, camera))
线性判别分析(linear discriminant analysis)
- LDA是一种监督学习的降维技术，也就是说它的数据集的每个样本是有类别输出的，这点和PCA不同。PCA是不考虑样本类别输出的无监督降维技术。