搜索资源列表
OLDA
- 正交线性判别分析（Orthogonal Linear Discriminant Analysis），可以用于数据降维上面。-orthogonal linear discriminant analysis (Orthogonal Linear Discriminan t Analysis), can be used for cutting down the data above.
C__MATLAB7_work_temporary_twodlpp2
- 2维LPP（2维局部映射保护）代码，可以对图像等二维数据进行降维。-two-dimensional LCDs (two-dimensional mapping of local protection) code, two-dimensional images of data dimensionality reduction.
drtoolbox.tar
- 一个基于matlab的数据降维工具箱,包括MDS,LEE等方法
guizhou
- 利用主成分分析法对BP神经网络的输入参数进行降维，然后进行网络的训练，PCA-BP处理的结果同单一的bp相比，不仅提高了网络的收敛速度，而且提高了网络对预测数据分类的精度
lda
- 非线性降维方法 可以应用于高维数据的机器学习-Nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
pca
- 非线性降维方法 可以应用于高维数据的机器学习-Nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
empca
- EMPCA算法的函数代码，附带有训练测试数据集，用于特征降维等方面。-Algorithm EMPCA function code, attached to the test data set there is training for the characteristics of dimensionality reduction and so on.
PCA
- 用于模式识别中的PCA降维输入数据data和option。data是一个矩阵，每一行代表一个样本。option是选择降维到多少维。-[eigvector, eigvalue] = PCA(data, options) [eigvector, eigvalue] = PCA(data)
31767689LPP_MatlabCode
- lPP 用于数据降维 很好用 希望能派上用场-LPP be use to dimension reduction
DDDCCT_PCAi
- 离散余弦变换，然后主成分分析进行数据降维，用于于模式识别，如人脸识别，掌纹，表情，指纹等识别。 -Discrete cosine transform, and then principal component analysis for data dimensionality reduction ffor pattern recognition, such as face recognition, palmprint, face, fingerprint and other identific
jiangwei
- 在科学研究中，我们经常遇到对大量高维数据进行处理的问题，本文是对数据降维算法的简要总结。-In scientific research, we often encounter the problem of handling a large number of high-dimensional data, the article is a brief summary of the data dimensionality reduction algorithm.
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",tt[1]- """ Principal c
dicbelevel
- 人工智能的经典资料，PCA和RobustPCA等算法，可用于数据降维-Classic artificial intelligence data, PCA and RobustPCA algorithms can be used for data dimensionality reduction
code
- 数据降维与流形学习，方便初始学者学习数据降维方法和流形学习。-Data dimension reduction and manifold learning
PCA
- 对训练数据进行主成分分析，得到降维矩阵，然后根据降维矩阵求得测试数据降维之后的矩阵。-The training data, principal component analysis, to obtain reduced dimensional matrix, and the matrix obtained after the dimensionality reduction test The dimensionality reduction matrix.
DBN
- 基于python的DBN实现，可以用来数据降维。(Implementation of DBN based on Python)
kpca
- 在matlab上面通过kpca，实现大数据降维算法(Dimensionality reduction algorithm for large data)
Autoencoder_Code
- Hilton用玻尔兹曼机实现数据降维的源代码(Hinton Science06 Reducing the dimensionality of data with neural networks)
lle
- lle用来处理高维数据降维，经检验此程序运行效果很好(LLE is used to deal with dimensionality reduction of high-dimensional data. It is proved that the program runs well.)
lle
- 实现非线性的数据降维，很简单的代码，比较容易明白(Achieve nonlinear data dimensionality reduction, very simple code, easier to understand.)