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pls
- 所谓偏最小二乘法，就是指在做基于最小二乘法的线性回归分析之前，对数据集进行主成分分析降维，下面的源码是没有删减的，GreenSim团队免费提供您使用，转载请注明GreenSim团队（http://blog.sina.com.cn/greensim）。 -The so-called partial least squares method, this means doing the least square method based on
pca
- PCA数据降维，利用MATLAB进行开发学习-PCA of data dimensionality reduction, using MATLAB to develop learning
lpp
- Laurens van der Maaten 编写的LPP算法，与 Deng Cai版本不同，大家比较下-This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7b. (C) Laurens van der Maaten Tilburg University, 2008
2
- 高 维 数 据 特 征降维研究综述-中文文本处理-Characteristics of high-dimensional data dimensionality reduction Survey
tiji
- 水库梯级优化调度，采用POA算法，算法简单可行，有效解决求解梯级水库中的维数灾问题-Cascade reservoir optimal operation, using POA algorithm is simple and feasible, an effective solution to solve the cascade reservoirs in the curse of dimensionality problem
base_plots_Merge_PCs
- Principal Components Analysis (PCA) is used to compress data in such a way that the least information is lost. It does so by truncating data and thereby leaving out the data which is of the least importance to the inform
SVM
- In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via
dyl1
- 利用支持向量机对T-S型模糊系统建模的方法，结合BP算法对参数进行优化，从一定程度上解决模糊系统建模所存在的模型结构复杂、维数灾、泛化能力不强和实时性差等问题。-this paper analyzed the approach that applied support vector machines to create novel model in the T-S fuzzy system, combined with BP algo
Som_clustering
- 基于VC++的Som聚类算法程序。SOM是一种通过自组织竞争学习网络实现数据的分类和降维可视化神经网络模型。内附算法的原理说明以及详细的程序调用说明及运算结果。是初学者的很好的入门材料-Based on VC++ program of Som clustering algorithm. SOM is a competitive learning through self-organizing network for data class
CovarianceApplications
- 数据挖掘课程的课件，关于协方差矩阵 有关于协方差矩阵的介绍，例子，应用 用在关于维度的降低上-data mining,Covariance Matrix Applications,Dimensionality Reduction
PCA
- PCA的简单小程序，对图像做去相关和降维-the simple program of PCA，used to a picture s decorrelation and dimensionality reduction
KECA
- Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010. We introduce kernel entropy component analysi
KLFDA
- 基于局部Fisher准则的非线性核Fisher辨别分析，应用于有监督的特征提取与高维数据的有效降维。-Kernel Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction.
manifold.tar
- manifold learning of none linear dimensionality reduction in matlab
PCA
- 主分量分析,用于高维数据降维或提取目标特征。程序精简，效率高. -Principal Component Analysis is used to make data dimensionality reduction or extract target characteristics。
Face_Recognition_Based_on_PCA_Comparative_Study.ra
- 主成成份分析( PCA) 方法是人脸识别技术中常用的一种一维特征抽取方法。传统PCA 方法用于人脸识别常常面临图像维数高,直接计算量的问题。为了解决这2 个问题,人们对PCA 进行了改进,提出并实现了多种基于PCA 的人脸识别。对3 种基于PCA 的人脸识别方法做了理论上的研究和实验上的性能比较。实验结果表明PCA + 2DPCA 是其中综合效果最好的一种方法。-Principal component analysis into (PC
drtoolbox
- 对于特征维数降维的matlab工具箱，包括PCA LDA ＰＰＣＡ 等-Matlab Toolbox for Dimensionality Reduction (v0.7.1- June 2010)
NonlinearDimensionalityReductionbyLocallLinearEmbe
- 一篇2000年流行的降维文章。学习降维必看。-Nonlinear Dimensionality Reduction by Locally Linear Embedding
PCA_C
- PCA 应用于数据矩阵降维，压缩数据！广泛应用于各种行业。 Author: F. Murtagh- Principal Components Analysis or the Karhunen-Loeve expansion is a classical method for dimensionality reduction or exploratory data analysis. One refer
mds
- 本代码是关于Multi-Dimensional Scaling（MDS）的代码，可以用于特征提取、特征选择，或是矩阵降维。-This file is part of the Matlab Toolbox for Dimensionality Reduction v0.4b. You are free to use, change, or However,