搜索资源列表
FPCA_MRM_pub
- 不动点连续近似奇异值分解算法用于解决矩阵填充的代码-Fixed point and Bregman iterative methods for matrix rank minimization
KSVD_Matlab_ToolBox
- 稀疏编码,去噪,奇异值分解 MATLAB-Sparse coding, denoising, singular value decomposition MATLAB
svdPdeconvPmodulate
- 对矩阵的奇异值分解,信号的解卷积,信号的调制-matrix singular value decomposition,deconvolution,modulation
chaos
- 使用matlab编写的用wolf法求Lyapunov指数的算法程序 假近邻法(False Nearest NeighborFNN)计算嵌入维的Matlab程序 一类基于奇异值分解的Lyapunov指数计算方法-Matlab program using Matlab prepared wolf method and the Lyapunov exponent of the false nearest neighbor algorithm program (False Nearest Nei
SVD-source-code
- 使用QR分解算法的奇异值分解源代码及注释-Singular value decomposition using the QR decomposition algorithm source code
tsvd_fft
- 基于fft的截断奇异值分解 (TSVD)正则化方法-TSVD: Truncated Singular Value Decomposition: TSVD
MatrixSingularValue
- 一般实矩阵的奇异值分解,功能检验通过,大家放心使用-Real matrix singular value decomposition, functional tests have been passed, we rest assured that the use
svd
- 矩阵的奇异值分解,程序编写比较公正,可读性强-The singular value decomposition, programming fair, readable
MATLAB
- 用MATLAB写得离散小波-奇异值分解的算法。嵌入水印信息-dwt-svd watermark algorithm
gaodengshuxue
- 可实现的算法:软件说明: 1.全主元高斯约当消去法2.LU分解法3.追赶法4.五对角线性方程组解法5.线性方程组解的迭代改善6.范德蒙方程组解法7.托伯利兹方程组解法8.奇异值分解9.线性方程组的共轭梯度法10.对称方程组的乔列斯基分解法11.矩阵的QR分解12.松弛迭代法第2章插值1.拉格朗日插值2.有理函数插值3.三次样条插值4.有序表的检索法5.插值多项式6.二元拉格朗日插值-The algorithm can be realized: Software Descr iption:
MatrixCalculator
- 2.1 矩阵类设计 2.2 矩阵基础运算 2.3 实矩阵求逆的全选主元高斯-约当法 2.4 复矩阵求逆的全选主元高斯-约当法 2.5 对称正定矩阵的求逆 2.6 托伯利兹矩阵求逆的特兰持方法 2.7 求行列式值的全选主元高斯消去法 2.8 求矩阵秩的全选主元高斯消去法 2.9 对称正定矩阵的乔里斯基分解与行列式的求值 2.10 矩阵的三角分解 2.11 一般实矩阵的QR分解 2.12 一般实矩阵的奇异值分解 2.13 求广义
CodesaImages
- 用于指纹检测等,利用图像的梯度方向,获得局部主导方向。Principal Component Analysis (PCA),包含有高斯金字塔分层,SVD奇异值分解,内含测试图像-Used for fingerprint detection, etc. Using the gradient direction of image to get local leading direction. Principal Component Analysis (PCA), contains a gaussi
C-Program
- 雅克比过关法算法求矩阵的奇异值分解,是雅克比算法的优化,提高计算速度-Jacobian immigration law algorithms singular value decomposition of a matrix, Jacobi algorithm optimization, improve computing speed
MVDR
- 何子述《现代信号处理及其应用》第6章课后习题代码,基于奇异值分解的MVDR方法进行信号频率估计的仿真实验-He sub-described " modern signal processing and its applications" in Chapter 6 Homework code MVDR method based on singular value decomposition of the signal frequency estimation simulation
SVD
- 矩阵的奇异值分解,使用fortran实现-The singular value decomposition of the matrix using fortran achieve
SCD_CPP
- 数值计算中SVD分解算法,其中包含SVD算法的C++实现头文件,源文件,程序测试代码,以及测试结果文件等,可以很好的帮助你理解SVD奇异值分解算法-Numerical calculation SVD decomposition algorithm, which the SVD algorithm C++ header files, source files, program testing code, and test results file can help you understand t
Analyzing-Spatially-varying-Blur
- 我们提出一种新方法用于单幅图像的空间变化模糊识别,在图像中的每个局部小块,这个局部模糊被选择在一个候选PSFS有限集中间,用一个极大似然方法。我们打算用广义似然减少参数的数量,用广义奇异值分解限制计算值,但是要对图像做出适当的边界假设。-We present a new approach for spatially varying blur identification using a single image. Within each local patch in the image,
SVD-theorem
- 奇异值分解定理,短小精悍的一篇文章,讲的很清楚-Singular value decomposition theorem, dapper article said very clearly
L1_SVD
- 利用压缩感知实现波达方向估计,运用奇异值分解对接收信号进行降维,再利用L1范数进行估计-Using compressed sensing DOA estimation using singular value decomposition to reduce the dimensionality of the received signal, and then using the L1 norm estimate
SVD-MOD
- C环境下实现矩阵的奇异值分解,实际验证之后证明效果不错-Singular value decomposition of the matrix C environment, proof of good results after the actual verification