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single
- 使用奇异值分解来帮助求解最小二乘问题，特别是在方程系数矩阵不满秩的情况下。-SGELSD computes the minimum-norm solution to a real linear least * squares problem: * minimize 2-norm(| b- A*x |) * using the singular value decomposition (SVD) of A. A is an M-by-N * matrix which
Incremental-SVD-updates
- 增量奇异值分解算法，来至MIT大学的wingate教授，含3个源码.-Given the thin SVD of a matrix (X=USV ), update it in a number of interesting ways, while preserving the rank of the result. svd_update.m- update the SVD to be [X+ A *B]=Up*Sp*Vp (a general matrix update). add
mySVD
- 输入矩阵X和维数d进行SVD分解，使得Xhat = U*S*V 是X的所有秩为d的近似中最好的一个- Accelerated singular value decomposition.Xhat = U*S*V is the best approximation (with respect to F norm) of X among all the matrices with rank no larger than ReducedDim.
SVD
- calculate low rank matrix using SVD
backgroud-model2
- 针对传统背景建模存在的问题，文中基于低秩矩阵恢复原理，直接从视频序列中分离出前景物体和背景模型。已有低秩矩阵恢复算法的迭代计算过程中涉及大量的奇异值分解，而这些奇异值分解一般非常耗时且不够简洁，文中在非精确增广拉格朗日乘子法中引入线性时间奇异值分解算法，以得到更加有效的背景建模算法。基于 实际视频序列实验，结果表明该改进算法具有更好的建模效果和较少的运算时间。-In this paper，a novel method is present based on low-rank matrix r
MAT
- 仿MATLAB矩阵C++运算库,包括加、减、乘、除、点加、点减、点乘、点除、赋值、转置、rank、det、eig、svd、pinv、power等的运算。inv运算使用pinv运算。最难实现的是非方阵的除法。-MatLab Matrix simulator
pcadenoise
- 矩阵 pca或者低秩方法去噪，利用svd分解，实现对图像矩阵的去噪，该方法支持对rgb图像的去噪。使用代码请 文章中表明出处，感谢。 感谢重庆市研究生科研创新项目支持，项目号CYS16183(image denoise by low-rand regularizer or pca method. the low rank is evaluted by svd, and this method is also support for rgb image.)
chen2014
- LWI-SVD: Low-rank, Windowed, Incremental Singular Value Decompositions on Time-Evolving Data Sets
Generalized Inverse Matrix
- 使用Hermite标准型、SVD分解和满秩分解求矩阵的广义逆，在此基础上求解线性方程组的解。(Hermite's canonical form, SVD decomposition and full rank decomposition are used to find the generalized inverse of the matrix, and then the solution of the linear equation system is solved.)