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Palm-biometrics-using-UDP
- This paper develops an efficient classification algorithm called UDP, which reduces the high dimension of sample to low dimensional subspace simply said as dimensionality reduction.UDP takes into account both the local and non-local quantities. It ca
FFLDbasedFacea
- 基于fisherface的人脸图像识识别，采用lda降维的方法来识别人脸图像 -Knowledge of the facial image recognition based on fisherface lda dimensionality reduction method to identify the face image
PPPCCALDAC
- PCA(主成分分析法)、LDA(线性判别法)两种方法是主要的的线性降维法，有非常好的效果，希望对大家能够有用！ 已通过测试。 -PCA (Principal Component Analysis), LDA (linear discriminant method) two methods are the main linear dimensionality reduction method, with very good results, we hope to be useful! Has
PCAPLDA
- 这个程序是做课程设计用的，希望对有需要的同学进行有帮助，PCA+LDA经典人脸识别算法，先用PCA降维，再用LCA降维-This procedure is done with the course design, and I hope there is a need for students to help, PCA+LDA classical face recognition algorithms, first PCA dimension reduction, reuse LCA dimens
LDPCCODE
- 这个程序是在国外的网站下载，希望对有需要的同学有用，PCA+LDA经典人脸识别算法，先用PCA降维，再用LCA降维-This program is downloaded from the website in a foreign country, I hope to needy students useful, PCA+LDA classical face recognition algorithms, first PCA dimension reduction, reuse LCA dimen
PCAPLDA
- PCA+LDA人脸识别，PCA降维到N-C,（N为训练样本数，C为类别数）使得Sw非奇异，主要是解决小样本，数据集为ORL，每类取9（可改）个图片-PCA+LDA recognition, PCA dimensionality reduction to NC, (N is the number of training samples, C is the number of categories) make Sw nonsingular, mainly to resolve the small s
CAN-code
- Clustering and Projected Clustering with Adaptive Neighbors -We proposed a CAN clustering algorithm with adaptive neighbors, the learned similarity matrix can be directly used for clustering, without having to perform K-means or other discretizatio
PCAPLDA
- PCA(主成分分析法)、LDA(线性判别法)两种方法是主要的线性降维法，有很好的效果，希望对大家能够有用！-PCA (Principal Component Analysis), LDA (Linear Discriminant method) two methods are the main linear dimensionality reduction method, have very good results, in the hope that everyone can be usefu
8-PLDAPPPPPPP
- Dimensionality reduction is one of the important preprocessing steps to handle high-dimensional data. Linear discriminant analysis (LDA) is a classical and popular approach for this purpose. LDA finds an optimal linear transformation, which max
LLE
- lle降维，可以参考非线性降维的方法，感觉没lda好用，比pca还行(LLE dimension reduction)
drtoolbox
- 包含多种常用维数优化算法及应用，PCA , ICA, LDA，LFA LPP，KPCA 等等。(There are many Dimensionality Reduction methods including PCA , ICA, KICA and so on.)