There's a part of Nathan Halko's dissertation referenced on algorithm page running comparison. In particular, he was not able to compute more than 40 eigenvectors with Lanczos on wikipedia dataset. You may refer to that study.
On the accuracy part, it was not observed that it was a problem, assuming high level of random noise is not the case, at least not in LSA-like application used there. That said, i am all for diversity of tools, I would actually be +0 on deprecating Lanczos, it is not like we are lacking support for it. SSVD could use improvements too. On Thu, Aug 1, 2013 at 3:15 AM, Fernando Fernández < [email protected]> wrote: > Hi everyone, > > Sorry if I duplicate the question but I've been looking for an answer and I > haven't found an explanation other than it's not being used (together with > some other algorithms). If it's been discussed in depth before maybe you > can point me to some link with the discussion. > > I have successfully used Lanczos in several projects and it's been a > surprise to me finding that the main reason (according to what I've read > that might not be the full story) is that it's not being used. At the > begining I supposed it was because SSVD is supposed to be much faster with > similar results, but after making some tests I have found that running > times are similar or even worse than lanczos for some configurations (I > have tried several combinations of parameters, given child processes enough > memory, etc. and had no success in running SSVD at least in 3/4 of time > Lanczos runs, thouh they might be some combinations of parameters I have > still not tried). It seems to be quite tricky to find a good combination of > parameters for SSVD and I have seen also a precision loss in some examples > that makes me not confident in migrating Lanczos to SSVD from now on (How > far can I trust results from a combination of parameters that runs in > significant less time, or at least a good time?). > > Can someone convince me that SSVD is actually a better option than Lanczos? > (I'm totally willing to be convinced... :) ) > > Thank you very much in advance. > > Fernando. >
