monstropizza's profile - activity

For now I'm just getting the first column of each matrix, like:

cv::SVD::compute(A, s, u, v);
u = u.col(0);
v = v.col(0);

With this, I'm getting a mean error of less than 0,0002 in my Wstar matrix (comparing with matlab result).

Anyone has a better idea than this?

Hi all, I'm using OpenCV to implement a PLS algorithm, and I need to run SVD in a big matrix. But I don't want all the singular values, just the largest one.

In matlab I can do this by calling:

[u,s,v] = svds(A, 1);

In OpenCV I'm doing:

cv::SVD::compute(A, s, u, v);

Anyone has any idea?

thx