svd

I am constantly getting this error. I am sure the matrix does not have any non-numeric entries. I also tried imputing the matrix, did not work. Anyone know what the error might be? fileUrl <- "https://dl.dropboxusercontent.com/u/76668273/kdd.csv"; download.file(fileUrl,destfile="./kdd.csv",method="curl"); kddtrain <- read.csv("kdd.csv"); kddnumeric <- kddtrain[,sapply(kddtrain,is.numeric)]; kddmatrix <- as.matrix(kddnumeric); svd1 <- svd(scale(kddmatrix)); You have columns composed of all zeroes. Using scale on a column of all zeroes returns a column composed of NaN . To solve this, remove

1利用矩阵转换不变的特性。 最经典的是SVD（奇异值分解），生成的矩阵相互转换。在使用的过程中，能够抵抗几何攻击。但早期的SVD数字水印算法出现虚警率的问题。 2扩频模式 这个是经典的实现算法，模拟信道传输，需要DSP方面的技术，这方面我很缺乏，就不细讲了。 3同步检测 canny边缘检测是经典的方法，特别对于旋转攻击，检测到角点，计算出偏移的角度，实现旋转纠正。 4 特征点提取。经典的是SIFT算法，但时间复杂度高，SURF就继承过来，并且时间复杂度得到改善。在结合一些数学工具的改进，能使图像获得更多的特征点。我现在正在研究，希望能有所收获，发篇好的论文，求祝福。 来源： CSDN 作者： 杨宝涛 链接： https://blog.csdn.net/zhongriqianqian2076/article/details/51996664

I've got a sparse Matrix in R that's apparently too big for me to run as.matrix() on (though it's not super-huge either). The as.matrix() call in question is inside the svd() function, so I'm wondering if anyone knows a different implementation of SVD that doesn't require first converting to a dense matrix. The irlba package has a very fast SVD implementation for sparse matrices. You can do a very impressive bit of sparse SVD in R using random projection as described in http://arxiv.org/abs/0909.4061 Here is some sample code: # computes first k singular values of A with corresponding singular

I'm new to parallel programming using GPU so I apologize if the question is broad or vague. I'm aware there is some parallel SVD function in the CULA library, but what should be the strategy if I have a large number of relatively small matrices to factorize? For example I have n matrices with dimension d , n is large and d is small. How to parallelize this process? Could anyone give me a hint? My previous answer is now out-of-date. As of February 2015, CUDA 7 (currently in release candidate version) offers full SVD capabilities in its cuSOLVER library. Below, I'm providing an example of

I'm trying to write a program that gets a matrix A of any size, and SVD decomposes it: A = U * S * V' Where A is the matrix the user enters, U is an orthogonal matrix composes of the eigenvectors of A * A' , S is a diagonal matrix of the singular values, and V is an orthogonal matrix of the eigenvectors of A' * A . Problem is: the MATLAB function eig sometimes returns the wrong eigenvectors. This is my code: function [U,S,V]=badsvd(A) W=A*A'; [U,S]=eig(W); max=0; for i=1:size(W,1) %%sort for j=i:size(W,1) if(S(j,j)>max) max=S(j,j); temp_index=j; end end max=0; temp=S(temp_index,temp_index); S