Hey Prasen, As I was kinda getting at over IM, for the order-3 tensor case, you can do
things like promote your scalars in your "document" vectors to be matrices, and then run the same kinds of decompositions on the resultant vectors as the matrix case, including do random-pre-projection. You can only get so much information this way (you're effectively reducing by a trace across some sub-tensors along the way), but it is better than nothing. But yeah, at least from a procedural standpoint, the Martinsson/Halko/Tropp gaussian pre-processing steps should be pretty portable to whatever fancy techniques are done on your tensors. Where you go from there is up to you! -jake On Sun, Nov 22, 2009 at 8:48 PM, prasenjit mukherjee < [email protected]> wrote: > Hi Jake, > Do you intend to contribute some of the Random Indexing code ? I > am working on a multi-way clustering problem and was thinking of using > tensor SVD to do that. In that context was wondering if anyone has > used Random Indexing to solve Higher Order SVD problem. I guess we > can extend the current 2d approach to higher dimensions while > generating the context vectors via iterating over the individual > contexts. > > My concern is that ( still working that out ) whether I am violating > any other constraints between the non-reducing dimensions. > > -Prasen > > On Sun, Nov 22, 2009 at 10:37 PM, Jake Mannix <[email protected]> > wrote: > > <snipped/> > > > The machinery to do the above in parallel on "ridiculously big" data on > > Hadoop > > should be coming in soon with some of the stuff I'm working on > contributing > > to Mahout. > > > > -jake > > >
