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[Windows Develop] 矩阵所有运算 DL : 1: 矩阵的转置、行列式、秩，逆矩阵求法，矩阵的三角分解、qr分解，对称正定矩阵的乔里斯基分解及行列式值，奇异值分解，广义逆的奇异值分解，矩阵特征值与特征向量的各种计算方法-all kinds of computational method of transposition of matrix, determinant, rank, inverse of matrix,triangle decomposition, qr decomposition, cholesky decomposition and determinant value based on symmetry positive definite matrix, decomposition of singular value, decomposition of generalized inversal singular value, eigenvalue and eigenvector
Update : 2008-10-13 Size : 81.53kb Publisher : 球球
[Other] InvMat DL : 0: 利用矩阵的奇异值分解压缩图像，先将图像分块，对各块中的每个对应象素内积得到相关矩阵A，然后对A进行奇异值分解，选择n（n<N）个较大的特征值对应的特征向量，利用该向量与各块图像内积来压缩和恢复图像-matrix of singular value decomposition image compression, image first block of the block of each pixel corresponding to be associated with the plot matrix A, and then A singular value decomposition, the choice of n (4; N) 000 eigenvalues larger the corresponding eigenvector, the use of the Vector with the block images within the plot to image compression and restoration
Update : 2008-10-13 Size : 1.22kb Publisher : 王志气
[Speech/Voice recognition/combine] lpcdaopu DL : 0: 可以求出语音信号的LPC倒谱特征向量，该特征向量在语音信号分析中得到了广泛的应用。-voice signal can be obtained by LPCCEP eigenvector, the eigenvector of the voice signal analysis has been widely used.
Update : 2008-10-13 Size : 3.46kb Publisher : 黄羽翔
[Other resource] pca.m DL : 1: 本程序的运行环境为matlab6.5，在command window下输入数据x和a，然后调用函数pca（x，a）（即本程序的主程序）输入的两个参数的意义是：一个是样本数据x，另外一个是主成分累积贡献率的一个闸值，作为选定主成分个数的一个重要数据。即可得到样本的协方差矩阵，相关矩阵，相关矩阵的特征根及特征向量，主成分个数和主成分负荷矩阵。 -the procedures for the environment matlab6.5. under the command window in the input data and a x, then the function call pca (x, a) (that is the main program of the procedures) and input parameters of the two is the meaning of : a sample data is x, Another is a principal component of the cumulative contribution rate of a gate value, as a principal component selected a number of important data. Samples can be the covariance matrix, the correlation matrix, a correlation matrix eigenvalue and eigenvector. Principal component and the number of principal components load matrix.
Update : 2008-10-13 Size : 1.43kb Publisher : 郑可可
[Other] tuxiangchuli_doc+cold DL : 0: 所谓基于内容图像的检索是指由图像分析软件对输入的图像先进行图像分析，根据图像中物体或区域的颜色(color)、形状(shape)或纹理(texture)等特征以及这些特征的组合，自动抽取特征，在将输入图像存入图像库的同时将其相应的特征向量也存入与图像库相连的特征库。-so-called content-based image retrieval is by image analysis software on the importation of the first images for image analysis. According to image objects or regional color (color). Shape (shape) or texture (texture), and other characteristics of these features combined, Automatic feature extraction. in the input image into the image of the corresponding eigenvector also credited with images linked to the characteristics of the library.
Update : 2008-10-13 Size : 69.24kb Publisher : 大海
[Mathimatics-Numerical algorithms] 雅可比过关法求实对称矩阵特征值与特征向量 DL : 0: 雅可比过关法求实对称矩阵特征值与特征向量-Jacobian clearance France realistic symmetric matrix eigenvalue and eigenvector
Update : 2025-02-19 Size : 1kb Publisher : 啊啊
[Internet-Network] qiutezhengzhidefangfa DL : 0: 利用C语言用矩阵迭代法求矩阵的特征值和特征向量-C language matrix iterative method for matrix eigenvalue and eigenvector
Update : 2025-02-19 Size : 1kb Publisher : 陈微
[File Format] maxeig DL : 0: Maximum eigenvalue and the corresponding eigenvector.-Maximum and the corresponding eigenvalue eigenvector.
Update : 2025-02-19 Size : 1kb Publisher : 金岳
[Algorithm] GivensHouseholder_eigenvectors DL : 0: GivensHouseholder法求解特征向量和特征值，公司所做项目中就是采用此法求解对称矩阵的特征向量和特征值，仅供参考！-Method GivensHouseholder eigenvector and eigenvalue, the company has done projects that use this method to solve symmetric matrix of eigenvectors and eigenvalues, for reference purposes only!
Update : 2025-02-19 Size : 7kb Publisher : cjs
[Algorithm] ppca DL : 0: Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal ％ component subspace U of dimension PPCA_DIM using a centred covariance matrix X. The variable VAR contains the off-subspace variance (which is assumed to be spherical), while the vector LAMBDA contains the variances of each of the principal components. This is computed using the eigenvalue and eigenvector decomposition of X.-Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA (X, PPCA_DIM) computes the principal component subspace U of dimension PPCA_DIM using a centred covariancematrix X. The variable VAR contains the off-subspace variance (whichis assumed to be spherical ), while the vector LAMBDA contains thevariances of each of the principal components. This is computedusing the eigenvalue and eigenvector decomposition of X.
Update : 2025-02-19 Size : 1kb Publisher : 西晃云
[Algorithm] ch14 DL : 0: 常用算法的C程序。主要内容包括多项式的计算、复数运算、随机数的产生、矩阵运算、矩阵特征值与特征向量的计算、线性代数方程组的求解、非线性方程与方程组的求解、插值与逼近、数值积分、常微分方程组的求解、数据处理、极值问题的求解、数学变换与滤波、特殊函数的计算、排序和查找。-Commonly used algorithm of C procedures. Include polynomial terms, complex computation, random number generation, matrix operations, matrix eigenvalue and eigenvector calculations, linear algebra equations, nonlinear equations with the equations, interpolation and approximation, numerical integration, solving ordinary differential equations, data processing, for solving extremum problems, mathematical transformation and filtering, the calculation of special functions, sorting and search.
Update : 2025-02-19 Size : 13kb Publisher : 周泽华
[Mathimatics-Numerical algorithms] eigenvalue DL : 0: 矩阵的本征值以及本征向量求解的数值程序，包括QR方法，householder方法等-Matrix eigenvalue and eigenvector of the numerical solving procedures, including the QR method, householder methods
Update : 2025-02-19 Size : 2.32mb Publisher : 叶飞
[matlab] Invariant_Line_Segment_Matching DL : 0: function Invariant_Line_Feature_Matching ___DESCRIPTION___ Compare segmented line pairs as 4 dimentional line pair features ( Q1 , Q2 , Drelative , D? ) Example : Invariant_Line_Feature_Matching ( ) ___REFERENCE___ Paper 1 : Line Feature Matching Technique Based on an Eigenvector Approach Park, Lee, Lee - Ideal - CV and Im. Und. - 77, 263-283 - (2000)- function Invariant_Line_Feature_Matching ___DESCRIPTION___ Compare segmented line pairs as 4 dimentional line pair features ( Q1 , Q2 , Drelative , D? ) Example : Invariant_Line_Feature_Matching ( ) ___REFERENCE___ Paper 1 : Line Feature Matching Technique Based on an Eigenvector Approach Park, Lee, Lee- Ideal- CV and Im. Und.- 77, 263-283- (2000)
Update : 2025-02-19 Size : 27kb Publisher : Mehmet
[Algorithm] EigenvalueVectorRealSymmetryJacobi DL : 0: 矩阵特征值与特征向量的计算，实对称阵特征值及特征向量雅可比法-Matrix eigenvalue and eigenvector calculations, real symmetric matrix eigenvalue and eigenvector Jacobi Method
Update : 2025-02-19 Size : 1kb Publisher : shenzhou
[Communication-Mobile] ev_method DL : 0: This file forms an estimate of the frequency spectrum using eigenvector algorithm (Johnson & DeGraaf 1990).
Update : 2025-02-19 Size : 1kb Publisher : shashi
[File Format] Eigenvector-Radiosity DL : 0: Eigenvector Radiosity 一篇老外写的很经典的辐射度算法的论文，研究引擎的有福了。-Eigenvector Radiosity a foreigner writing classic radiosity algorithm papers, research engine of the Blessed.
Update : 2025-02-19 Size : 2.59mb Publisher : bluesky
[Algorithm] eigenvector DL : 0: 用于实现特征值和特征向量，这边是实现最大特征值，然后相对应的特征向量-get eigenvalue and eigenvector
Update : 2025-02-19 Size : 3kb Publisher : bai
[Program doc] An-Adaptive-Block-Based-Eigenvector-Equalization. DL : 0: An Adaptive Block-Based Eigenvector Equalization for Time-Varying Multipath Fading Channels.In this paper we present an adaptive Block- Based EigenVector Algorithm (BBEVA) for blind equalization of time-varying multipath fading channels. In addition we assess the performance of the new algorithm for different configurations and compare the results with the least mean squares (LMS) algorithm. The new algorithm is evaluated in terms of intersymbol interference (ISI) suppression, mean squared error (MSE) and by examining the signal constellation at the output of the equalizer.
Update : 2025-02-19 Size : 255kb Publisher : abd091
[Other] eigenValues-and-eigenVector DL : 0: 各种矩阵特征值与特征向量的计算算法，包括雅克比、QR等-Various matrix eigenvalue and eigenvector calculation algorithms, including Jacobi, QR, etc.
Update : 2025-02-19 Size : 12kb Publisher : 段涛
[matlab] The eigenvector of the eigenvalue DL : 0: 求解一个矩阵的最大特征根及最大特征根对应的特征向量，并对其归一化，求出权向量。主要用来解决数学建模层次分析法中需求解的各指标权重值。以及附带了几个动态图形的制作编程(The eigenvector of the largest eigenvalue and the largest eigenvalue of a matrix is solved and normalized, and the weight vector is obtained. It is mainly used to solve the weight of each index in demand analysis of mathematical modeling.And programming with several dynamic graphics)
Update : 2025-02-19 Size : 4kb Publisher : sixsixsixwk

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