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PCA_NN
- PCA（主成分分析）算法被广泛应用于工程和科学研究中，本报告主要从PCA的基本结构和基本原理对其进行研究，常规的PCA算法主要采用线性算法，通过研究论证发现线性的PCA算法存在着许多不足，比如线性PCA算法不能从线性组合中把独立信号成分分离出来，主分量只由数据的二阶统计量—自相关阵确定，这种二阶统计量只能描述平稳的高斯分布等，因此必须对其进行改进，经改进后的PCA算法有非线性PCA算法、鲁棒算法等。我们通过PCA算法在直线（平面）中拟和的例子说明了PCA在工程中的应用。本例子采用的是成分分析中的
stprtool.rar
- 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，Feature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines,
feipca.rar
- 非线性PCA算法matlab程序段，完成独立分量分析的主分量分解,Non-linear PCA algorithm matlab program segment, the completion of independent component analysis of the principal component decomposition
kernel_pca
- Kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with
stprtool15aug08
- 统计模式识别算法包，包括线性分类算法，SVM，PCA，LDA，EM，k-means分类等多种常用的模式识别算法。-Statistical pattern recognition algorithm package, including a linear classification algorithm, SVM, PCA, LDA, EM, k-means classification and many other commonly used pattern recognition algori
HumanFacePCA(Matlab)
- 线性判别方法，人脸数据库上的PCA程序，基于Matlab，供学习参考-Linear discriminant method, human face database of PCA procedures, based on Matlab, for study and reference
gauss
- 数值分析高斯——列主元消去法主程序 说明如下： ％ a----input,matrix of coefficient ％ b----input,right vector ％ sol----output,returns the solution of linear equation-Gauss numerical analysis- out PCA elimination main program as follows: a---- input, matrix of coeffi
featureExtraction
- 该程序包实现了模式识别中的两个特征提取算法，主成分分析PCA和线性判别分析LDA。采用C++语言编写，开发环境VS。 程序包还提供了两个测试样本文件。-The package to achieve the recognition of the two feature extraction algorithm, principal component analysis PCA and linear discriminant analysis LDA. Using C++ language, dev
pattern-recognition-simulation
- 用mushrooms数据对模式识别课程讲述的各种模式分类方法[线性分类，Bayesian分类，Parzen窗，KNN]和特征选择和降维方法[PCA,LDA]进行了模拟，并给出了各类分类方法的结果，-It s the simulations about linear classification ,Bayesian ,Parzen and KNN of pattern recognition .And ,It gives the results.
drtoolbox.tar
- 这是一个MATLAB工具箱包括32个降维程序，主要包括 pca,lda,MDS等十几个程序包，对于图像处理非常具有参考价值- ，This Matlab toolbox implements 32 techniques for dimensionality reduction. These techniques are all available through the COMPUTE_MAPPING function or trhough the GUI. The following techn
KPCA
- 为解决PCA不适合多指标综合分析中非线性主成分分析的问题 ,采用核主成分分析 (kpca)方法 ,对我国不同地区 16种腐乳的品质进行了综合评价。 -PCA is not suitable to address the many indicators of a comprehensive analysis of non-linear principal component analysis of the problem, using Kernel Principal Component An
vb-solution-of-linear-equations
- 利用vb解线性方程组，利用Gauss列主元消去法-Vb solutions using linear equations using Gauss elimination method out PCA
pca
- PCA:Principal Components Analysis It computes Principal Component Analysis, i.e., the linear transform which makes data uncorrelated and minize the reconstruction error.
KERNEL-PCA
- Kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a n
pca-matlab
- PCA又为主成分分析，是一种特征提取方法， 它通过线性变换寻找一组最优的单位正交向量基，用他们的线性组合来重构原样本-PCA and the main component analysis, a feature extraction method, which by finding a set of optimal linear transformation unit orthogonal base vectors, with their linear combination to recons
pca
- 详细的线性PCA计算过程，详细的线性PCA计算过程-The linear PCA detailed calculation process
用PCA（非工具包，自写）实现LDA
- 上了一门统计分析的课程，所有课程所学内容均不允许使用工具包，特自写PCA，实现LDA线性分类，希望可以与大家分享，一起学习参考，
PCA-ICA
- 实现了主元分析（PCA）和独立分量分析（ICA）相关信号处理。非线性降维。(Implements Principal Component Analysis (PCA) and Independent Component Analysis (ICA) correlation signal. Non-linear dimension reduction using kernel PCA.)
gpldecha-e-pca-d542a9b
- PCA是一种非线性降维方法特别适合于概率分布，得到了指数族PCA的POMDPs压缩。(Matlab implementation of E-PCA which is a non-linear dimensionality reduction method particularly suited for probability distributions, see the paper Exponential Family PCA for Belief Compression in POMDPs.)
pca-lda
- 主成分分析法和线性判别分析常用来对原始数据进行简单的数学分析(Principal component analysis and linear discriminant analysis are usually used for simple mathematical analysis of raw data.)