I bet page rank in mllib in spark finds stationary distribution much faster. On Feb 17, 2014 1:33 PM, "peng" <[email protected]> wrote:
> Agreed, and this is the case where Lanczos algorithm is obsolete. > My point is: if SSVD is unable to find the eigenvector of asymmetric > matrix (this is a common formulation of PageRank, and some random walks, > and many other things), then we still have to rely on large-scale Lanczos > algorithm. > > On Mon 17 Feb 2014 04:25:16 PM EST, Ted Dunning wrote: > >> For the symmetric case, SVD is eigen decomposition. >> >> >> >> >> On Mon, Feb 17, 2014 at 1:12 PM, peng <[email protected]> wrote: >> >> If SSVD is not designed for such eigenvector problem. Then I would vote >>> for retaining the Lanczos algorithm. >>> However, I would like to see the opposite case, I have tested both >>> algorithms on symmetric case and SSVD is much faster and more accurate >>> than >>> its competitor. >>> >>> Yours Peng >>> >>> On Wed 12 Feb 2014 03:25:47 PM EST, peng wrote: >>> >>> In PageRank I'm afraid I have no other option than eigenvector >>>> \lambda, but not singular vector u & v:) The PageRank in Mahout was >>>> removed with other graph-based algorithm. >>>> >>>> On Tue 11 Feb 2014 06:34:17 PM EST, Ted Dunning wrote: >>>> >>>> SSVD is very probably better than Lanczos for any large decomposition. >>>>> That said, it does SVD, not eigen decomposition which means that the >>>>> question of symmetrical matrices or positive definiteness doesn't much >>>>> matter. >>>>> >>>>> Do you really need eigen-decomposition? >>>>> >>>>> >>>>> >>>>> On Tue, Feb 11, 2014 at 2:55 PM, peng <[email protected]> wrote: >>>>> >>>>> Just asking for possible replacement of our Lanczos-based PageRank >>>>> >>>>>> implementation. - Peng >>>>>> >>>>>> >>>>>> >>>>> >>
