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Description: 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次成分（方差最小的成分），通过对结果的分析，我们可以看出，利用PCA算法可以得到较好的拟和结果。-PCA (Principal Component Analysis) algorithm has been widely used in engineering and science research, This report mainly from the PCA and the basic structure of the basic tenets of its research, Conventional PCA algorithm used mainly linear algorithm, found through research and demonstration linear PCA algorithm, there are many inadequate, For example, not linear PCA algorithm from the linear combination of the independent signal components separated, PCA data only from the second-order statistics-auto-correlation matrix to determine, Such second-order statistics can only describe a smooth Gaussian distribution, it is necessary to improve it. After the improvement of the PCA algorithm is nonlinear PCA algorithm, robust algorithm. PCA algorithm we passed the line (plane), and to be example
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Author: 东方云 |
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Description: 线性代数方程组的求解全选主元高斯消去法全选主元高斯约当消去法复系数方程组的全选主元高斯消去法-linear algebraic equations to solve all the main election yuan Gaussian Elimination entire election PCA about when Gaussian Elimination of complex equations of the entire election PCA Gaussian Elimination Act, etc.
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Size: 14336 |
Author: 樊继东 |
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Description: Ｃ＋＋解线性方程组
复系数方程组的全选主元高斯消去法-C++ solution of linear equations of complex equations of the entire election PCA Gaussian Elimination
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Size: 6144 |
Author: 张宁 |
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Description: 数值分析算法描述与习题解答，由清华大学徐士良教书，用C编写的各种数学算法。比如：托伯利兹型线性代数方程组的递推算法，全选主元高斯消去法解复系数线形代数方程组，复矩阵求逆的全选主元高斯-约当法，等；-numerical analysis algorithm description and answer the questions from Qinghua University XU Shi-liang, teaching C prepared by the various mathematical algorithms. For example : Toeplitz linear algebraic equations, a recursive algorithm, the whole election PCA Gaussian Elimination demultiplexing coefficient of linear algebraic equations, matrix inversion of the entire election PCA Gaussian-about when the law;
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Size: 88064 |
Author: qinzuoyi |
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Description: 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次成分（方差最小的成分），通过对结果的分析，我们可以看出，利用PCA算法可以得到较好的拟和结果。-PCA (Principal Component Analysis) algorithm has been widely used in engineering and science research, This report mainly from the PCA and the basic structure of the basic tenets of its research, Conventional PCA algorithm used mainly linear algorithm, found through research and demonstration linear PCA algorithm, there are many inadequate, For example, not linear PCA algorithm from the linear combination of the independent signal components separated, PCA data only from the second-order statistics-auto-correlation matrix to determine, Such second-order statistics can only describe a smooth Gaussian distribution, it is necessary to improve it. After the improvement of the PCA algorithm is nonlinear PCA algorithm, robust algorithm. PCA algorithm we passed the line (plane), and to be example
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Size: 454656 |
Author: 东方云 |
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Description: 这里面包含两个文件，可以实现主成分分析（PCA）算法和线性判别分析（LDA）算法。不过还需要CMatrix类的定义。-which contained two documents, can be achieved principal component analysis (PCA) algorithm and linear discriminant analysis (LDA) algorithm. But also need CMatrix class definition.
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Size: 3072 |
Author: hongbochen |
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Description: 现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。-existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher.
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Size: 2048 |
Author: 小弟 |
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Description: pca,用在人脸特征识别中,fisher线性判别
kpca,dwt2-pca used in facial recognition, fisher linear discriminant kpca, dwt2
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Size: 1129472 |
Author: verochen |
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Description: 基于神经元网络和 Linear Auto-Associative memory 和pca的人脸识别程序。-based on neural network and Linear Auto-Associative memory pca and face recognition procedures.
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Size: 5120 |
Author: 王大宝 |
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Description: 支持向量机(SVM)、线形鉴别分析(LDA)、主分量分析(PCA)和人工神经元网络(ANN)源代码-Support Vector Machine (SVM), linear discriminant analysis (LDA), principal component analysis (PCA) and artificial neural network (ANN) source code
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Size: 332800 |
Author: 李旸 |
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Description: 主成分分析方法（PCA），PCA算法的理论依据是K-L变换，通过一定的性能目标来寻找线性变换W，实现对高维数据的降维。-Principal component analysis (PCA), PCA algorithm is based on the theory of KL transform, through a certain performance targets to find the linear transformation W, the realization of high-dimensional data, dimensionality reduction.
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Size: 1024 |
Author: 李伟 |
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Description: PCA(主成分分析法)、LDA(线性判别法)
两种方法是主要的线性降维法，有很好的效果，希望对大家能够有用！-PCA (Principal Component Analysis), LDA (Linear Discriminant method) two methods are the main linear dimensionality reduction method, have very good results, in the hope that everyone can be useful!
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Size: 3072 |
Author: ruchong |
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Description: 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
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Size: 1024 |
Author: 高亚力 |
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Description: PCA:Principal Components Analysis
It computes Principal Component Analysis, i.e., the linear transform which makes data uncorrelated and minize the reconstruction error.
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Size: 1024 |
Author: Lara |
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Description: 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 non-linear mapping.-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 non-linear mapping.
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Size: 3072 |
Author: rem |
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Description: 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 reconstruct the original sample
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Size: 5120 |
Author: liru |
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Description: 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
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Size: 1024 |
Author: huiyu |
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Description: 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.)
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Size: 15169536 |
Author: SONAH~
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Description: 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.)
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Size: 7041024 |
Author: SONAH~
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Description: 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.)
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Size: 2048 |
Author: cc2358564166 |
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