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Description: 求取矩阵的协方差矩阵，本人以前编写的，大家要用的话，估计要进行适当的修改-strike covariance matrix of matrices, I used to prepare, we use the words, it is estimated to proper changes
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Size: 1206272 |
Author: 冯涛 |
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Description: A series of .c and .m files which allow one to perform univariate and bivariate wavelet analysis of discrete time series. Noother wavelet package is necessary -- everything is contained in this archive. The C-code computes the DWT and maximal overlap DWT. MATLAB routines are then used to compute such quantities as the wavelet variance, covariance, correlation, cross-covariance and cross-correlation. Approximate confidence intervals are available for all quantities except the cross-covariance and cross-correlation.
A set of commands is provided. For a description of this example, please see http://www.eurandom.tue.nl/whitcher/software/. -A series of. C and. M files which allow one to perform univariate and bivariate wavelet analysis of discrete time series. Noother wavelet package is necessary- everything is contained in this archive. The C-code computes the DWT and maximal overlap DWT. MATLAB routines are then used to compute such quantities as the wavelet variance, covariance, correlation, cross-covariance and cross-correlation. Approximate confidence intervals are available for all quantities except the cross-covariance and cross-correlation.A set of commands is provided . For a description of this example, please see http://www.eurandom.tue.nl/whitcher/software/.
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Size: 37888 |
Author: yupenghui |
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Description: VC 6.0下的主成分分析代码，包括关系矩阵，协方差矩阵，以及因子分析的功能-VC 6.0 Principal Component Analysis code, including the relationship matrix, covariance matrix, and the function of factor analysis
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Size: 241664 |
Author: Eniak |
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Description: 马氏距离判别(MahalanobisDistanceDiscrimination)的演示过程,求均值向量,协方差矩阵及线形判别函数-Mahalanobis distance criterion (MahalanobisDistanceDiscrimina tion) demonstration process for the mean and covariance matrix and linear discriminant function
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Size: 3072 |
Author: 陈伟 |
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Description: （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象；
（2）将所有子图象按列相接变成一个81维的行向量；
（3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量；
（4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。
（5）求出所有子块的特征向量。
-(1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.
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Size: 64512 |
Author: ly |
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Description: SMI(协方差矩阵求逆)算法是波束合成常用的算法。-SMI (covariance matrix inversion) algorithm is commonly used synthetic beamforming algorithms.
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Size: 1024 |
Author: 郑耀 |
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Description: 本文提出一种新的相干波束形成方法，利用内插变换对相干背景下的真实阵列进行虚拟平移，得到多个虚拟平移后的信号协方差矩阵；对其进行平均后，所得到的相干信号协方差矩阵具有满秩性。利用一般的波束形成法求出最优权向量。该方法解决了五阵元均匀圆阵下二维相干信号的波束形成问题。-This paper presents a new coherent beamforming methods, the use of interpolation transform the context of coherent arrays of virtual real translation, from a number of virtual post-translational signal covariance matrix their average after by Coherent signal covariance matrix has full rank of. Beamforming using the general method to derive the optimal weight vector. The method to solve a five-element uniform circular array of two-dimensional signals coherent beamforming problem.
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Size: 1330176 |
Author: 关成 |
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Description: EKF-SLAM Simulator
This version of the simulator uses global variables for
all large objects, such as the state covariance matrix.
While bad programming practice, it is a necessary evil
for MatLab efficiency, as MatLab has no facility to avoid
gratuitous memory allocation and copying when passing
(and modifying) variables between functions. With this
concession, effort has been made to keep the code as
clean and modular as possible.
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Size: 18432 |
Author: 唐新杰 |
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Description: ％ EM algorithm for k multidimensional Gaussian mixture estimation
％
％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
％
％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％- EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates
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Size: 3072 |
Author: Shaoqing Yu |
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Description: 进行多元统计分析基类代码及接口的实现
元统计分析 基类 声明
提供矩阵数据格式 及矩阵的 加减乘 运算及多元统计中常用的求 样本均值 协方差 方法-Multivariate statistical analysis for the base class code and the interface element statistical analysis of base class declaration data format and the matrix matrix addition and subtraction, multiplication and Multivariate Statistics used in order to sample mean covariance method
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Size: 43008 |
Author: 胡子春 |
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Description: 又一种增量人脸学习算法——参考文献“Candid Covariance-Free Incremental Principal Component Analysis.”-Face another incremental learning algorithm- References Candid Covariance-Free Incremental Principal Component Analysis.
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Size: 1024 |
Author: 陈静 |
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Description: 程序设计步骤：
1、去均值
2、计算协方差矩阵及其特征值和特征向量
3、计算协方差矩阵的特征值大于阈值的个数
4、降序排列特征值
5、去掉较小的特征值
6、去掉较大的特征值(一般没有这一步）
7、合并选择的特征值
8、选择相应的特征值和特征向量
9、计算白化矩阵
10、提取主分量-Programming steps: 1 to mean 2, calculation of covariance matrix and its eigenvalues and eigenvectors 3, the calculation of covariance matrix eigenvalues greater than the threshold number of 4, in descending order of eigenvalue 5, remove the smaller eigenvalue 6, remove the larger eigenvalue (usually not this step) 7, the combined choice of eigenvalues 8, select the appropriate eigenvalues and eigenvectors 9, computing whitening matrix 10, principal component extraction
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Size: 1024 |
Author: lrx |
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Description: 极化SAR图像中的将协方差矩阵数据进行PAULI分解的程序-Polarization SAR images will covariance matrix data Pauli decomposition process
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Size: 1024 |
Author: 王文光 |
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Description: The angles in degrees of the two spatially propagating signals
Compute the array response vectors of the two signals
Compute the true covariance matrix
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Size: 250880 |
Author: zhaocuiqin |
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Description: 一种Kalman滤波系统误差及其协方差矩阵的半参数估计方法的研究文章-A Kalman filter system error covariance matrix and the semi-parametric estimation methods of research papers
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Size: 354304 |
Author: 王华 |
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Description: 本程序编程语言为C，主要用来对遥感训练数据进行处理，得到covariance矩阵。-This program is used to generate 3 files:mean file,covariance matrix of the training set, and inverse covariance matrix for training set.
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Size: 4096 |
Author: 李会利 |
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Description: Simulation of the Spatial Covariance Matrix
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Size: 81920 |
Author: lilla |
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Description: This MATLAB code computes the covariance matrix of a random variable
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Size: 1024 |
Author: Mohammed |
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Description: Fast Human Detection from Videos Using Covariance Features
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Size: 395264 |
Author: roro2020 |
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Description: 该代码用于实现视觉目标跟踪研究中的协方差跟踪，能够把多种时空特征融合于统一的模型中，在实现视觉目标跟踪时具有较好的鲁棒性，而且其维数等于使用特征的数量，与各个特征的维数无关，因此，其计算复杂度较小，实时性较好。-This code is used to realize covariance tracking in the research field of visual object tracking of computer vision. It enables fusion of various spatial-temporal features into an unified object feature model, hence robust to noise. In addition, its dimension is equal to the number of the features used and independent of the dimensions of the features used. Therefore, it satisfies the requirement of real-time application due to its low computation cost.
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Size: 133120 |
Author: 朱亮亮 |
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