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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.
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Size: 1549 |
Author: 浇洒距离 |
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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.
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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
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Author: 东方云 |
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Description: Matlab codes for Robust PCA multivariate control chart-Robust PCA multivariate control chart mainly consists two steps:
Step1
Calculates the robust mean and the robust
covariance of original dataset using the
minimum covariance determinant (MCD).
In MCD technique, finding a subset
containing half of the data such that its
covariance matrix has the lowest determinant, then using this subset to
calculate the robust mean and the robust
covariance matrix (Hubert, Rousseeuw, &
Branden, 2005)
Step2
Standardize data using robust mean and
robust standard deviation from Step1.
Apply PCA analysis, calculate the principalcomponent score matrix Y=ZA, where Z is the robust standardized data matrix, and
A is p*p matrix of eigenvectors (also called principalcomponents)
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Size: 1024 |
Author: Jianxin Zhang |
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Description: RobustPCA 是最近提出的一种非常新的图像矩阵分解算法，该算法具有对噪声不敏感、能处理高维图像数据的特点。这是论文作者提供的 MATLAB 实现代码。-Oct 2009
This matlab code implements the augmented Lagrange multiplier method for
Robust PCA.
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Author: bsmsht |
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Description: 强健性多元回归工具箱，强健性PCA，强健性PLS-robust regression toolbox，including robust PCA，Robust PLS
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Size: 104448 |
Author: 王凌波 |
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Description: ALM fo Robust PCA,实现RPCA-ALM fo Robust PCA
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Size: 354304 |
Author: chenyu |
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Description: Robust PCA and subspace tracking
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Size: 9216 |
Author: stone |
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Description: 用ALM实现 PCA算法，做模式识别的一看就懂，自己用的不错。-This matlab code implements the augmented Lagrange multiplier method for Robust PCA.
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Size: 355328 |
Author: 吴明 |
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Description: 非精确ALM解决PCA算法的例子，用后效果不错，发上来分享。-This matlab code implements the inexact augmented Lagrange multiplier method for
Robust PCA.
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Size: 355328 |
Author: 吴明 |
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Description: 利用 svt算法解决 Rpca算法的放缩问题，牛刀小试，大家见谅！-Solves the Robust PCA relaxation
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Size: 2048 |
Author: 吴明 |
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Description: 能够检测异常值 数据降维 特征提取 鲁棒主成分分析方法-Robust PCA
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Author: 小兔子 |
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Description: robust PCA的应用实例 很有代表性的方法-this paper proposed a approach of robust
face recognition by exploiting the sparse error component obtained by RPCA.
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Author: zbh_wj |
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Description: PCA基于Iris数据集，用于统计学习作业。是非常好用且鲁棒的方法。有利于学习统计学习PCA和matlab。-PCA based on Iris data set, used in statistical learning assignments.It is very convenient and robust method.Is advantageous to the study of statistical learning PCA and matlab.
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Size: 2048 |
Author: 刘磊 |
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Description: 鲁棒主成分分析 低秩与稀疏矩阵分解 增广拉格朗日 图像重建、去噪-robust pca low-rank and sparse matrix decomposition
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Size: 353280 |
Author: gbyzzj |
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Description: Robust Principal Component Analysis with Complex Noise
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Size: 3811328 |
Author: Tariq Sadad |
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Description: RPCA,Robust PCA，matlab-RPCA，matlab，robust pca
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Size: 1024 |
Author: szk |
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Description: 用matlab实现了 inexact ALM 算法(the inexact augmented Lagrange multiplier method for Robust PCA)
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Size: 1024 |
Author: annivy
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Description: 用于分析Robust PCA对应的MATLAB程序，将一个矩阵分解为低秩和稀疏矩阵的形式(This paper analyzes the MATLAB program corresponding to Robust PCA, and decomposes a matrix into a form of low rank and sparse matrix.)
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Size: 4096 |
Author: 你懂得啊
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Description: 用于拉格朗日函数分解运算的PCA算法，MATLAB程序实现(PCA algorithm for decomposition operation of Lagrange function)
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Size: 2212864 |
Author: 你懂得啊
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