A very wise comment about clustering nlp data from someone who knows a lot
about it (Fernando Pereira of UPenn - a quick google search will show off
his qualifications if you aren't familiar with him). This is in reply to a
message about Weka's implementation of clustering, but it certainly
applies in general. Note that Fernando stresses the need for
dimensionality reduction, which is exactly what we're trying to do
in SenseClusters with SVD.

--
Ted Pedersen
http://www.d.umn.edu/~tpederse

---------- Forwarded message ----------
Date: Sun, 27 Jun 2004 16:43:43 -0400
From: Fernando Pereira <[EMAIL PROTECTED]>
To: Jonathan Elsas <[EMAIL PROTECTED]>
Cc: [EMAIL PROTECTED]
Subject: Re: [Wekalist] EM cluster empty exception


On Jun 23, 2004, at 4:07 PM, Jonathan Elsas wrote:

> I've received this exception a lot with the EM implementation in weka.
>  I had previously posted an email to the weka-list re. this problem
> (although I can't seem to find it in the wekalist archives online),
> but no one responded.  We've been using the EM for text data, which is
> also very high-dimensional (and sparse).  I don't know if that has
> something to do with the EM implementation producing this exception.

Clustering text data or any very high dimension sparse data is very
tricky, and it's unlikely that a generic latent variable clustering
model will do anything useful without careful data preparation. There
are (at least) two reasons for this. First, the predominance of zeros
in the data vectors causes drastic overfitting, which may be the cause
of the empy cluster exception (I don't know the details of the WEKA
method, but I've seen similar problems with other implementations).
Second, a standard Gaussian mixture model is hard to fit in high
dimension <http://charlotte.ucsd.edu/users/dasgupta/papers/focs2.ps>.
Some form of dimensionality reduction is critical for getting useful
results <http://charlotte.ucsd.edu/users/dasgupta/papers/random.ps>.

-- F


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