you may want to take a look at mahout-376. https://issues.apache.org/jira/browse/MAHOUT-376
On Sun, Sep 5, 2010 at 10:37 PM, RadimRehurek <[email protected]>wrote: > Hello Dmitriy, > > > i came very close to try out Ted's layout for stochastic svd (the way i > > understood it, with block QR solvers on mapper side instead of > gram-schmidt > > on the whole scale as Tropp seems to suggest ) and not following mahout's > > general architecture, but I wasn't actually able to carve out enough > time > > for this. But sooner or later somebody will implement that, and then > stuff > > you are right -- I kicked myself to finally implementing that paper after > seeing Daisuke Okanohara's "redsvd" :-) > > * http://code.google.com/p/redsvd/wiki/English , in-core stochastic > decomposition on top of eigen3, a beautiful C++ template library > > In any case, it looks like the power iterations are really needed already > for mid-sized problems like Wikipedia. Either that, or use very aggressive > over-sampling (I use #samples=2*requested_rank at the moment, and it's not > enough). That means doing extra passes over the data to compute > Y=(A*A^T)^q*A*G instead of just Y=A*G :-( ... unless there is a clever way > to avoid it. > > Radim > > > On Sun, Sep 5, 2010 at 7:33 PM, RadimRehurek <[email protected]> > wrote: > > > > > See that module's docstring; reading the input is slower than > processing it > > > with the stochastic decomposition. > > > > > > In short: in order for distributed computing to make sense > > > (performance-wise), the data would already need to be pre-distributed, > too. > > > > > > This is true in Hadoop, so I guess stochastic decomposition is an algo > > > where MAHOUT could really make a difference on terabyte+ problems. > > > > > > Radim > > > > > > > > > >
