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From Jake Mannix <>
Subject Re: tf-idf + svd + cosine similarity
Date Tue, 14 Jun 2011 23:09:49 GMT
On Tue, Jun 14, 2011 at 3:35 PM, Dmitriy Lyubimov <> 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 (, line
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
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.


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