svd

My goal is to transfer a coordinate in perspective from a known rectangle (for example a 800 by 600 screen) to a quadrangle that is skewed/rotated. To do so i found this, which was extremely helpful: Transforming captured co-ordinates into screen co-ordinates I guess there are more solutions to the problem. 1 by making triangles out of your quadrangle and apply some mathematical function which i could not solve yet. OR using the H-matrix which comes out of the formula A=USVt. which seemed nice because once you have the correct H-matrix you can transfer any coordinate pretty easy as explained

转载：http://redstonewill.com/1529/ 普通方阵的矩阵分解（EVD） 我们知道如果一个矩阵 A 是方阵，即行列维度相同（mxm），一般来说可以对 A 进行特征分解： 其中，U 的列向量是 A 的特征向量，Λ 是对角矩阵，Λ 对角元素是对应特征向量的特征值。 举个简单的例子，例如方阵 A 为： 那么对其进行特征分解，相应的 Python 代码为： 1 import numpy as np 2 3 A = np.array([[2,2],[1,2]]) 4 lamda, U = np.linalg.eig(A) # 特征向量和特征值 5 print('方阵 A', A) 6 print('特征值 lamda', lamda) 7 print('特征向量 U', U) 8 9 # 输出 10 # 方阵 A [[2 2] 11 # [1 2]] 12 # 特征值 lamda [3.41421356 0.58578644] 13 # 特征向量 U [[ 0.81649658 -0.81649658] 14 # [ 0.57735027 0.57735027]] 特征分解就是把 A 拆分，如下所示： 其中，特征值 λ1=3.41421356，对应的特征向量 u1=[0.81649658 0.57735027]；特征值 λ2=0.58578644，对应的特征向量 u2=

I wonder why there is sign difference in result for SVD computing in Matlab and OpenCV. I input the same matrix 3.65E+06 -2.09E+06 0 YY = -2.09E+06 2.45E+06 0 0 0 0 [U,S,V] = svd(YY);//Matlab -0.798728902689475 0.601691066917623 0 V = 0.601691066917623 0.798728902689475 0 0 0 1 cv::SVD::compute(YY, S, U, V);//opencv 0.798839 -0.601544 0 V = 0.601544 0.798839 0 0 0 1 I know that they use the same algo, why there is sign difference? Thanks Which version of OpenCV are you using? From http://code.opencv.org/issues/1498 it seems recent versions of OpenCV no longer use LAPACK to do SVD (as used by

I have been working on latent semantic analysis lately. I have implemented it in java by making use of the Jama package. Here is the code: Matrix vtranspose ; a = new Matrix(termdoc); termdoc = a.getArray(); a = a.transpose() ; SingularValueDecomposition sv =new SingularValueDecomposition(a) ; u = sv.getU(); v = sv.getV(); s = sv.getS(); vtranspose = v.transpose() ; // we obtain this as a result of svd uarray = u.getArray(); sarray = s.getArray(); varray = vtranspose.getArray(); if(semantics.maketerms.nodoc>50) { sarray_mod = new double[50][50]; uarray_mod = new double[uarray.length][50];

I would like to implement Singular Value Decomposition (SVD) in PHP. I know that there are several external libraries which could do this for me. But I have two questions concerning PHP, though: 1) Do you think it's possible and/or reasonable to code the SVD in PHP? 2) If (1) is yes: Can you help me to code it in PHP? I've already coded some parts of SVD by myself. Here's the code which I made comments to the course of action in. Some parts of this code aren't completely correct. It would be great if you could help me. Thank you very much in advance! si28719e SVD-python Is a very clear,

SVD也是对矩阵进行分解，但是和特征分解不同，SVD并不要求要分解的矩阵为方阵。假设我们的矩阵A是一个 $A=U\sumV^2$ 来源： https://www.cnblogs.com/xcxy-boke/p/11407636.html

（1）SVD（Singular value decomposition）：奇异值分解，矩阵分解的算法之一。 在数据分析中的输入矩阵A一般是非奇异矩阵，而使用SVD可将A分解成一个对角阵B，形式如下： A = P B Q 这里的B是不带有隐特征的，但由于SVD计算量太大，一般都用MF模型 （2）MF（Matrix Factorization）：也是一种矩阵分解。形式如下： A = （P的转置）Q 隐特征在P和Q之内 （3）FM（Factorization Machine）： FM模型是用于推荐系统的一种新提出来的推荐模型，用于预测用户对某个该用户没有选择过的项目的评分，依据评分的高低针对用户进行推荐。FM模型也是一种有监督的学习过程，也就是说要有训练集，通过训练集的数据进行参数训练来得到模拟推荐的模型的最优。 隐特征在（Vi，Vj）中 来源： https://blog.csdn.net/woshiliulei0/article/details/99978276