On Tue, Jun 14, 2011 at 3:35 PM, Dmitriy Lyubimov <dlieu.7@gmail.com> 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
