Thanks for the quick response. So I will create a new diagonal matrix with the reciprocals of the eigenvalues, and multiply by that. I took a look at the slides (very nice presentation!), but it seems that I won't even need to go this far, as I should be able to take E^(-1) x U^(T) x docvector, and U is available from the output of ssvd. I'm basing this assumption on pages 2/3 of [1].
Thanks again for the help, Chris [1] https://cwiki.apache.org/MAHOUT/stochastic-singular-value-decomposition.data/SSVD-CLI.pdf On Fri, Jun 29, 2012 at 4:31 PM, Sean Owen <sro...@gmail.com> wrote: > Well the inverse of a diagonal matrix like that is just going to be a > diagonal matrix holding the reciprocals (1/x) of the values. That much > is easy. But you need to invert more than that to fold in. > > I admit even I don't know the details of the Mahout implementation > you're using, but I imagine the overall principle is the same as the > fold-in described in ... oh wait, look at that, in a preso I posted a > while ago: http://www.slideshare.net/srowen/matrix-factorization Look > at the last few slides; I think it's kind of a useful / simple way to > think of it. > > Sean > > On Fri, Jun 29, 2012 at 10:27 PM, Chris Hokamp <chris.hok...@gmail.com> > wrote: > > Hi all, > > > > I'm trying to implement Latent Semantic Indexing using the mahout ssvd > > tool, and I'm having trouble understanding how I can use the output of > ssvd > > Mahout to 'fold' new queries (documents) into the LSI space. > Specifically, > > I can't find a way to multiply a vector representing a query by the > inverse > > of the matrix of singular values - I can't find a way to solve for the > > inverse of the diagonal matrix of singular values. > > > > I can generate the output matrices using ssvd, and compare document/term > > vectors using cosine similarity, but I'm stumped when it comes to > folding a > > new document into the space. > > > > Any thoughts or guidance would be appreciated. > > > > Cheers, > > Chris >
