《机器学习》课上的作业，PCA和LDA降维，尽管网上很多，但很少注释，另外细节上也没注意。这里有很详细的注释。另外还附上一个Naive贝叶斯分类器，大家可以作比较。附带的图像包是OLR人脸。ReducedDim为想要提取的特征数，不是百分比！," Machine learning" classes on the homework, PCA and LDA dimensionality reduction, even though a lot of online, but f

matlab图像处理工具相，使用了主成分分析，ANN，SVM等方法。-This toolBox used in the image processing(feature extraction and classification) PCA,LDA,ICA,DCT,RBF,RBE,GRNN,KNN,minimum distance,SVM, and others

基于fisherface的人脸图像识别，采用lda降维的方法来识别人脸图像-This package implements a well-known FLD-based face recognition method, which is called Fisherface [1]. All functions are easy to use, as they are heavy commented. Furtheremore, a sample scr ipt is incl

包含数据，可以直接运行。采用LBP特征进行行人和非行人图像的分类，分类方法是LDA。-It contains data that can be run directly. Use LBP features pedestrian and non-pedestrian image classification, classification is LDA.