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From: Ted Dunning <ted.dunning@gmail.com>
Date: Mon, 5 Jul 2010 23:27:04 -0700
Message-ID: <AANLkTinQ-zEqqzJepuJ2Lxp-UgorYCaWX5Ooi6Bsy9ju@mail.gmail.com>
Subject: Re: SVD and Clustering
To: user@mahout.apache.org
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Related to normalization, the original LSA team claimed better results with
tf.idf weighting.  I would tend to use log(1+tf) . idf instead.  I think
that term weighting of this sort is quite common.

Document level normalization is a bit less common.  It is common practice,
however, to not normalize documents but instead to drop the first
eigenvector on the theory that is where the document norm winds up anyway.
 I would imagine that normalizing documents to some degree would make the
numerics of computing the SVD a bit better and save the extra work of
computing and then throwing away that eigenvector.  The first eigenvector
also takes the load of centering the documents.

I do know that I have forgotten to toss that first eigenvector on several
occasions and been mystified for a time at how my results weren't as good.

On Mon, Jul 5, 2010 at 11:16 PM, Jake Mannix <jake.mannix@gmail.com> wrote:

> In my own experience, things like graphs (including bipartite graphs like
> ratings matrices) I normalized before *and* after, but text I don't (unit)
> normalize before, but do normalize after.
>
> The reasoning I use is that normalizing the rows of graphs has
> a meaning in the context of the graph (you're doing the PageRank-like
> thing of normalizing outflowing probability when looking at random
> walks, for example, or for ratings matrices, you're saying that
> everyone gets "one vote" to distribute amongst the things they've
> rated [these apply for doing L_1 normalization, which isn't always
> appropriate]), while I don't know if I buy the similar description of
> what pre-normalizing the rows of a text corpus.
>

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