I couldn't possible comment. If you were actually achieving the minimum, the results should have been the same subject to order and sign changes.
On Tue, May 24, 2011 at 6:15 PM, Dmitriy Lyubimov <[email protected]> wrote: > the reason i am asking is because i actually did that very scheme some > time ago and for whatever reason i got very mixed results. I assumed > that was because i better be doing it incrementally. > > > On Tue, May 24, 2011 at 6:13 PM, Dmitriy Lyubimov <[email protected]> > wrote: > > Or incremental SVD provides orthogonality of the singular vectors > > while LLL does not? (my best guess why they do it incrementally). > > > > On Tue, May 24, 2011 at 6:11 PM, Dmitriy Lyubimov <[email protected]> > wrote: > >> Thanks, Ted. > >> > >> On Tue, May 24, 2011 at 5:44 PM, Ted Dunning <[email protected]> > wrote: > >>> Heh? > >>> > >>> Are you referring to the Log linear latent factor code that I have in > my > >>> mahout-525 github repo? > >>> > >> > >> I am referring to LatentLogLinear class in your repo under lll branch. > >> > >>> > >>> > >>>> However, i never understood why this factorization must come up with r > >>>> best factors. I understand incremental SVD approach > >>>> (essentially the same thing except learning factors iteratively > >>>> guarantees we capture the best ones) but if we do it all in parallel, > >>>> does it create any good in your trials? > >>>> > >>> > >>> I don't understand the question. > >>> > >>> Are you asking whether the random projection code finds the best > (largest) > >>> singular > >>> values and corresponding vectors? If so, the answer is yes, it does > with > >>> high probability > >>> of low error. > >>> > >> > >> Well you have alternating scheme there, right? you do learn left > >> singular vectors, then you switch, find the right singular vectors, > >> but as far as i can tell you are not doing it the same way as > >> incremental SVD does > >> > >> Incremental SVD goes thru the entire dataset the same way but only for > >> 1 factor first. then it frozes it once testing rmse curve is flat and > >> starts doing the same for the second one. Intuitively it's clear that > >> the first pass this way finds the largest factor and the next one > >> finds the next largest etc. Hence there's a 'step' curve on RMSE chart > >> for this process as it switches from factor to factor. > >> > >> But in your case, it looks like you are learning all the factors at > >> once. Is it going to result into the same result as incremental SVD > >> algorithm? if yes, why did they even do it incrementally, for it's > >> clear incremental approach would require more iterations? > >> > >> (there's a mahout issue for incremental svd implementation btw). > >> > >>> > >>> Regarding the side information, I thought it was there but may have > made a > >>> mistake. > >>> > >> > >> which branch should i look at for the latest code? i looked at LLL > branch. > >> thanks. > >> > > >
