Description: 基于kernel pca的非线性降维算法，原文发表于神经计算杂志上，有兴趣者可以先看论文。-PCA-based kernel of nonlinear reduced dimension algorithm, the original published in the Journal of neural computation, those interested can read papers. Platform: |
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Author:武旗 |
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Description: 鲁棒控制器设计，由于RBF网络可以实现任意逼近的非线性关系，它的目标是要做到误差平方和最小，与非线性PCA的目标一致，所以上述非线性PCA的模型可以通过采用两个RBF网络来实现非线性正变换 和反变换 。RBF网络是一个三层前馈网络，隐层采用径向基函数作为激励函数。第一个RBF网络把高维空间的数据映射到低维空间（如图4），第二个RBF网络将前面网络输出的低维空间数据再映射到高维空间，实现数据恢复(如图5)。这两个网络分别进行训练。-robust controller design, as RBF networks can achieve arbitrary nonlinear approximation, Its goal is to achieve the minimum squared error, and nonlinear PCA have the same goal So these nonlinear PCA model may be adopted by two RBF networks to achieve nonlinear transformation and inverse transform. RBF network is a feed-forward network, hidden layer RBF function as an incentive. RBF a network of high-dimensional data mapping space to the low-dimensional space (figure 4), second RBF network will be in front of the output of low-dimensional space mapping data again to a high-dimensional space. data Recovery (figure 5). The two networks separately for training. Platform: |
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Author:浇洒距离 |
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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 Platform: |
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Author:东方云 |
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Description: 数值分析实验报告!包含多个实验！
实验一 非线性方程求根
实验二 线性代数方程组的解法
--------列主元Gauss消元法
实验三 线性代数方程组的解法
——Gauss-Seidel迭代法等-Numerical Analysis of the experimental report! Contains more than one experiment! Experiment 1 Experiment 2 roots of nonlinear equations of linear algebraic equations the solution out PCA Gauss elimination method experiment trilinear solution of algebraic equations- Gauss-Seidel iteration method, such as Platform: |
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Author:geoshion |
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Description: 主成分分析和偏最小二乘SquaresPrincipal成分分析（ PCA ）和偏最小二乘（ PLS ） ，广泛应用于工具。此代码是为了显示他们的关系，通过非线性迭代偏最小二乘（ NIPALS ）算法。
-Principal Component Analysis and Partial Least SquaresPrincipal Component Analysis (PCA) and Partial Least Squares (PLS) are widely used tools. This code is to show their relationship through the Nonlinear Iterative PArtial Least Squares (NIPALS) algorithm.
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Author:harrison |
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Description: This toolbox is an educational and recreative toolbox around recent ideas in the field of dimension reduction.
* PCA : classical Principal Componnent Analysis (linear projection).
* Nonlinear dimensionality reduction by locally linear embedding.
* Laplacian Eigenmaps for dimensionality reduction and data representation-This toolbox is an educational and recreative toolbox around recent ideas in the field of dimension reduction.
* PCA : classical Principal Componnent Analysis (linear projection).
* Nonlinear dimensionality reduction by locally linear embedding.
* Laplacian Eigenmaps for dimensionality reduction and data representation Platform: |
Size: 226304 |
Author:tra ba huy |
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Description: 一个集成了8种降维方法的GUI（包括常见的PCA、LLE、isomap、HLLE等线性与非线性将为方法）-An integrated eight kinds of dimensionality reduction methods GUI (including common PCA, LLE, isomap, HLLE, etc. will be linear and nonlinear methods) Platform: |
Size: 14336 |
Author:huang |
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Description: 在matlab环境中自动识别连通区域的大小,基于分段非线性权重值的Pso算法，有较好的参考价值，Matlab实现界面友好，脉冲响应的相关分析算法并检验，借鉴了主成分分析算法（PCA），对HARQ系统的吞吐量分析，通过虚拟阵元进行DOA估计。- Automatic identification in the matlab environment the size of the connected area, Based on piecewise nonlinear weight value Pso algorithm, There are good reference value, Matlab to achieve user-friendly, Related impulse response analysis algorithm and inspection, It draws on principal component analysis algorithm (PCA), HARQ throughput analysis of the system, Conducted through virtual array DOA estimation. Platform: |
Size: 6144 |
Author:bmisq |
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Description: 基于chebyshev的水声信号分析，有均匀线阵的CRB曲线，包括脚本文件和函数文件形式，包括面积、周长、矩形度、伸长度，现代信号处理中谱估计在matlab中的使用，借鉴了主成分分析算法（PCA），基于分段非线性权重值的Pso算法。- Based chebyshev underwater acoustic signal analysis, There ULA CRB curve, Including script files and function files in the form, Including the area, perimeter, rectangular, elongation, Modern signal processing used in the spectral estimation in matlab, It draws on principal component analysis algorithm (PCA), Based on piecewise nonlinear weight value Pso algorithm. Platform: |
Size: 8192 |
Author:ecwhgiqae |
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Description: 利用最小二乘法进行拟合多元非线性方程，借鉴了主成分分析算法（PCA），用MATLAB实现动态聚类或迭代自组织数据分析。- Multivariate least squares fitting method of nonlinear equations, It draws on principal component analysis algorithm (PCA), Using MATLAB dynamic clustering or iterative self-organizing data analysis. Platform: |
Size: 5120 |
Author:张翠来 |
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Description: 这是一个好用的频偏估计算法的matlab仿真程序,关于非线性离散系统辨识，Gabor小波变换与PCA的人脸识别代码。- This is a useful frequency estimation algorithm matlab simulation program, Nonlinear discrete system identification, Gabor wavelet transform and PCA face recognition code. Platform: |
Size: 5120 |
Author:mxeux |
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Description: 借鉴了主成分分析算法（PCA），利用最小二乘法进行拟合多元非线性方程，matlab程序运行时导入数据文件作为输入参数。- It draws on principal component analysis algorithm (PCA), Multivariate least squares fitting method of nonlinear equations, Import data files as input parameters matlab program is running. Platform: |
Size: 5120 |
Author:张根 |
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Description: Is a good learning materials to learn PCA feature extraction, Multivariate least squares fitting method of nonlinear equations, Face Recognition light treatment method. Platform: |
Size: 4096 |
Author:tangenjui
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