搜索资源列表
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
CCA_zq
- 用于特征降维，特征融合，相关分析等多元数据分析的典型相关分析Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis, canonical correlation analysis of Matlab code implementation.
DCCA_zq
- 用于特征降维，特征融合，相关分析等多元数据分析的鉴别型典型相关分析（DCCA）Matlab代码实现。-For feature reduction, feature fusion, multivariate data analysis and correlation analysis based identification of canonical correlation analysis (DCCA) Matlab code implementation.
GCCA_zq
- 用于特征降维，特征融合，相关分析等多元数据分析的广义典型相关分析（GCCA）Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis using generalized canonical correlation analysis (GCCA) Matlab code implementation.
LDA_zq
- 用于特征降维，特征融合，相关分析等多元数据分析的fisher鉴别分析（FLDA）Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis of the fisher discriminant analysis (FLDA) Matlab code implementation.
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.
PCAxmeas_fault1
- 主元分析方法（PCA）是一种基于多元统计分析的数据降维方法, 它利用过程变量间的相关关系, 建立正常工况下的主元模型, 通过检验新的数据样本相对于主元模型的背离程度, 从而发现异常和故障。 -Principal Component Analysis (PCA) is based on multivariate statistical analysis of the data reduction method, which uses the correlation between process
pcakpca
- 图像降维方法pca和kpca的matlab程序-Image dimensional reduction pca and kpca the matlab program
regularized-lda
- 正则化LDA，最新！对于图像降维与图像识别的研究有很重要的研究意义-Regularized LDA, the latest! Dimension reduction for images and image recognition of a very important research significance
lda
- 一个基于人耳模式识别的lda算法，可实现对高维矩阵的降维。-A pattern recognition based on human ear lda algorithm can realize high-dimensional matrix of dimension reduction.
jiangerweishuzuzhuanhuanweboxingshuzu
- 实用LabVIEW软件，实现将二维数据转换为波形数组，同时将波形文件保存-Practical LabVIEW, to achieve two-dimensional data into a waveform array, while the waveform file
SPSS
- 主成分分析的主要目的是希望用较少的变量去解释原来资料中的大部分变异，将我们手中许多相关性很高的变量转化成彼此相互独立或不相关的变量。通常是选出比原始变量个数少，能解释大部分资料中的变异的几个新变量，即所谓主成分，并用以解释资料的综合性指标。由此可见，主成分分析实际上是一种降维方法。-The main purpose of PCA is to use fewer variables to explain most of the variation of the original data will
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)
jiangwei1
- 降维状态观测器是非常重要的一类观测器 基于P变换的降维观测器设计方法，-Reduced-order state observer is a very important class of observer-based P Transform reduced order observer design method
LRQ
- 单级倒立摆LQR控制仿真 降维状态观测器的两种设计方法及算例-Inverted Pendulum Simulation Reduced LQR control of two state observer design method and examples
jiangweiguanceqi
- 降维状态观测器是非常重要的一类观测器，它使用状态反馈构成闭环系统的物理实现成为可能。降维状态观测器的设计方法很多，这里提出主要的两种：-Reduced-order state observer is a very important class of observer, which uses state feedback closed loop system, the physical realization possible. Reduced-order state observer desi
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 reference among many is: F. Murtagh
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,
topicmodal
- 文本降维的新技术。含隐语义分析，在文本分类、聚类等领域都有广泛前景。-A new text dimension reduction techniques. With implicit semantic analysis, text classification, clustering and other fields have a wide prospect.