The SVD does not in general give you eigenvalues of its input. Are you just trying to access the U and V matrices? they are also returned in the API. But they are not the eigenvectors of M, as you note.
I don't think MLlib has anything to help with the general eigenvector problem. Maybe you can implement a sort of power iteration algorithm using GraphX to find the largest eigenvector? On Fri, Aug 8, 2014 at 4:07 AM, Chunnan Yao <yaochun...@gmail.com> wrote: > Hi there, what you've suggested are all meaningful. But to make myself > clearer, my essential problems are: > 1. My matrix is asymmetric, and it is a probabilistic adjacency matrix, > whose entries(a_ij) represents the likelihood that user j will broadcast the > information generated by user i. Apparently, a_ij and a_ji is different, > caus I love you doesn't necessarily mean you love me(What a sad story~). All > entries are real. > 2. I know I can get eigenvalues through SVD. My problem is I can't get the > corresponding eigenvectors, which requires solving equations, and I also > need eigenvectors in my calculation.In my simulation of this paper, I only > need the biggest eigenvalues and corresponding eigenvectors. > The paper posted by Shivaram Venkataraman is also concerned about symmetric > matrix. Could any one help me out? --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
