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CodeGPCAPDASpectral
- 广义主成分聚类，用于高维数据降维聚类分析，比主成分分析更好一点-Generalized principal component clustering analysis is used to reduce the dimension of high-dimensional data, which is better than the principal component analysis.
pca
- 人脸搜索简单实现，使用主成分分析算法(PCA)，依赖opencv，对若干张人脸图片进行PCA降维处理，然后将输入人脸图片与降维后的数据做比较，根据权重输出结果，权重越大则人脸越相似-Simple face search, using the principal component analysis (PCA) algorithm, opencv, for a number of face images for PCA dimension reduction, and then the input
Laplacian_Eigenmaps
- 拉普拉斯特征映射算法，可实现高维信号降维或实现带内滤波降噪。-Laplace feature mapping algorithm, can achieve high dimensional signal dimensionality reduction or implement band noise filtering.
PCA-AND-PNN
- 应用主成分分析对数据降维，将得到的数据用于概率神经网络训练，进行模式识别。对于一组新数据，先计算主成分得分，再输入训练好的概率神经网络，就会得到识别结果，即改组数据属于何种类别。-Principal component analysis of the data reduction, data will be obtained for the probabilistic neural network training, pattern recognition. For a new set of d
PCAPMFA
- 在模式识别当中，MFA出现小样本问题。为解决这个问题。我们提出了PCA+MFA的方法。首先对其进行PCA预处理，然后在用MFA降维。-Small sample problems in pattern recognition, MFA. To solve this problem. We put forward the PCA+ MFA method. On the PCA pretreatment first, and then with MFA dimension reduction.
PCAPLPP
- 在人脸识别当中，针对LPP是一个局部化的线性降维处理，我们先提出来先对其进行预处理，然后再利用LPP。-In face recognition, in view of the LPP is a localization of linear dimension reduction processing, we first put forward the preprocessing, and then re-use LPP
mani
- 数据降维工具，包括统计方法和流形学习方法-Data reduction tools, including statistical methods, and manifold learning method
pca
- 基于主元分析的异常检测和故障诊断，用于对具有高度线性相关的测量数据进行分析和处理，其最终实现高维空间降维的目的。-Anomaly detection based on principal component analysis and fault diagnosis, used for highly linear correlation measurement data analysis and processing, its ultimate achieve the goal of higher
PCA_K
- PCA的思想为将图像的协方差矩阵分解，获得分解后的方向向量。然后将数据分别投影到某一个方向上去，获得与原图象近似的图像。当然,与最大特征值所对应的特征向量方向获得最好的图像。因此，PCA方法可以作为降维的一种方法。留下在某些方向较好的图像，而抛弃那些在另外一些方向上不好的图像。-PCA ideas as to decompose the covariance matrix of the image, the direction vector obtained after decompositio
icaplot
- ICA是20世纪90年代后期发展起来的一项新降维处理方法。其含义是把信号分解成若干个互相独立的成分，并将信号源按不同混合比例组合起来的多通道同步观察。也就是说，ICA是属于多导信号处理的一种方法。ICA技术本身是用来处理盲源分离问题的，但是随着近些年的发展，其他领域也提出了相应的应用。在信号处理、图像加密及计量经济学等等方面都展现了独特的应用价值。绘图-Surgery itself is used to handle the problem of blind source separation,
whitenv
- 白噪化处理，这是高光谱图像数据降维的前预处理，也称为球化，在数据降维中必须用到-White noise process, which is pretreatment hyperspectral image data dimensionality reduction, also called the ball in the data dimensionality reduction must be used
PCA
- 能够实现对多维数据的PCA主元分析，对数据降维，提取主元。-PCA principal component analysis can be achieved, for data dimensionality reduction, extraction PCA
drtoolbox.tar
- 用于降维的matlab工具包，包括PCA，LDA，LLE，等-Matlab Toolbox for Dimensionality Reduction Principal Component Analysis (PCA) Probabilistic PCA Factor Analysis (FA) Classical multidimensional scaling (MDS) Sammon mapping Linear Discriminant Analysis (LDA
PCA-LDA-LPP-SLLDA
- 图像降维的经典算法PCA,LDA，LPP,SLLDA程序，包含掌纹库，可以直接解压运行-The classic image dimensionality reduction algorithm of PCA, LDA, LPP, SLLDA program that contains palmprint , can direct decompression operation
PAC--Datamining
- PCA降维算法应用大数据挖掘中，在大数据环境下实现数据的降维，可按需要自行修改代码-PCA dimensionality reduction algorithm in data mining, in the big data environment for data dimension reduction, according to need to modify the code itself
dimen-reduc
- 用于图像或者信号高维特征的降维程序，包含KLPP,OLPP和PCA等程度-for dimensionality reduction
mani
- 此代码是关于流形学习，数据降维，代码中含有的主要方法是PCA，KPCA，MDS，KMDS，Laplacian等等，且代码作了可视化处理，界面效果完美-This code is on the manifold learning, data dimensionality reduction, the main method code is contained in PCA, KPCA, MDS, KMDS, Laplacian, etc., and the code visualization ma
KPCA-ORL
- 随着人脸识别越来越受到重视，而人脸识别中特征降维又很重要，所以特征降维算法也很重要。因此PCA降维算法很重要-Dimensionality reduction for Face Recognition
svm_matlab_facerecognition
- 利用PCA算法进行特征提取和数据降维，再用SVM算法进行人脸识别的程序，里面有人脸数据库！-Use PCA algorithm for feature extraction and data reduction, and then SVM algorithm recognition program, which was face !
2dpca
- 对人脸进行2DPCA提取特征并降维,提高人脸识别率-On people s faces 2D PCA feature extraction and dimensionality reduction, improve face recognition rate