搜索资源列表
binary2DPCA
- 双向二维主成分分析，可用于特征提取和数据降维。-binary 2DPCA is usually used for feature extraction and dada dimension reduction
KPCA
- 本方法使用sprtool，介绍了Kpca的应用，来进行高位数据降维，介绍三种核函数的应用，并附有结果图。-This method uses sprtool, introduced Kpca applications for high data dimensionality reduction, introduced three nuclear function, together with the results in Fig.
pca
- pca算法实现，实现了一个数据降维方面的经典算法PCA-pca algorithm to achieve a data classic dimensionality reduction algorithm PCA
kpcacsdn
- kpca的算法实现源码，实现了一个数据降维方面的经典算法kpca-kpca the algorithm source code, to achieve a data dimensionality reduction classical algorithm kpca
kpca_toy
- kpca的算法实现源码，实现了一个数据降维方面的经典算法kpca-kpca the algorithm source code, to achieve a data dimensionality reduction classical algorithm kpca
lowern
- 详细介绍了数据降维的方法，并讲了实现方法-Detailed data dimensionality reduction method, and about implementation methods
pca
- pca数据降维程序，包括取均值、计算协方差矩阵等详细步骤。-the pca Data dimensionality reduction procedures, including averaging, calculated covariance matrix detailed steps.
mani-real
- 人脸数据库数据降维的Matlab实现，可将高维的人脸数据降维到3维或2维-The face database data dimensionality reduction Matlab, the face of the high-dimensional data down to 3-dimensional or two-dimensional
123
- 数据降维，并且画出降维之后的图像，体现降维后的分类精度-Data dimensionality reduction, and draw the image after dimensionality reduction, reflecting the classification accuracy of dimensionality reduction
pca
- 主成分分析（Principal Copmponent Analysis，简称PCA）是一种常用的机遇变量协方差矩阵对信息进行处理、压缩和提取的有效方法。主成分分析，这种方法可以有效的找出数据中最“主要”的元素和结构，去除噪音和冗余，将原有的复杂数据降维，能够发掘出隐藏在复杂数据背后的简单结构。-PCA (Principal Copmponent Analysis, abbreviated PCA) is a commonly used
LDA
- LDA:线性判别分析方法。用于实现线性数据降维。采用K近邻分类器对数据进行分类-LDA: linear discriminant analysis method. Used to achieve linear data dimensionality reduction. Using K-nearest neighbor classifier for data classification
drtoolbox
- 国外的牛人写的软件，数据降维工具箱，包含了几十种常用的流形学习方法的源码，自带图形界面。-A foreign master write this dimension reduction toolbox,there s dozens of code of common manifold learning methods.
mani-isomap
- 流行学习isomap（有标注，便于更好理解等距映射方法在高维数据降维过程的实现）-Popular learning isomap (have annotations, facilitate better understanding isometric mapping method in the implementation of the high-dimensional data dimension reduction)
SDEmatlab
- 基于监督学习的一种非线性数据降维方法，可以很好地将低维特征从其所在的高维流形中提取出来，用于数据分类。-A nonlinear data dimension reduction based on supervised learning method, is a good way to the lower dimensional feature extracted from its place of higher dimensional
OLDA
- OLDA算法,可用于样本书和类别数较少的数据降维-OLDA algorithm
PCA1
- pca算法，用于数据降维，注释非常详细清晰，-PCA algorithm, for data dimensionality reduction, clear and very detailed notes,
HRPCA
- 能够检测异常值 数据降维 特征提取 鲁棒主成分分析方法-Robust PCA
PCA
- PCA算法实现对数据降维，train_sample为训练样本，train_class为训练样本的分类结果，test_sample为测试样本，test_class为测试样本的分类结果，可以从UCI下载数据集进行调用~-PCA algorithm for data dimensionality reduction, train_sample of training samples, train_class for the classific
machine-learning_PCA
- 环境为winpython 32bit 2.7.5.3 p = PCA() print u"均值化后的数据集为：",p.dataset( H:\\PCA_test.txt ) print u"协方差矩阵为：",p.COV() print u"特征向量为：",p.eig_vector()[1] tt = p.pc(dim=1) print "tt:",tt print u"新的维度数据集",t
PCA_Fisher
- matlab中PCA-fisher判别法，对数据降维有很好的效果，附件中是matlab2007源程序，不同的matlab版本需要稍微修改一下；-Discriminant law PCA-fisher MATLAB, to reduce the dimension of data have a good effect, accessory is the MATLAB2007 source code, a different version