thanks, Jake.
On Tue, Jun 14, 2011 at 4:09 PM, Jake Mannix <[email protected]> wrote: > On Tue, Jun 14, 2011 at 3:35 PM, Dmitriy Lyubimov <[email protected]> wrote: >> >> Normalization means that second norm of columns in the eigenvector >> matrix (i.e. all columns) is 1. In classic SVD A=U*Sigma*V', even if >> it is a thin one, U and V are orthonormal. I might be wrong but i was >> under impression that i saw some discussion saying Lanczos singular >> vector matrix is not necessarily orthonormal (although columns do form >> orthogonal basis). I might be wrong about it. >> > > LanczosSolver normalizes the singular vectors (LanczosSolver.java, line > 162), > and yes, returns V, not U: if U is documents x latent factors (so gives the > projection of each input document onto the reduced basis), and V is > latent factors x terms (and has rows which gives each show which > latent factors are made up of what terms). Lanczos solver doesn't keep > track > of documents (partly for scalability: documents can be thought of as > "training" your latent factor model), but they instead return the latent > factor by term "model": V. > > -jake >
