Hi Emanuelle, Rawi thanks for the tips. I read the docs as suggesting
the complexity is O(T*(N^2)*I)), rather than O(T*N^(2*I)), which would
be enormously more CPU intensive. But anyway, I'll follow Emanuelle's
suggestion of turning debugging on to get an idea of its rate of
progress, and then get back to you, thanks! Brian
------------------------------ Message: 2 Date: Wed, 12 Jan 2011
16:33:31 +0100 From: Emanuele Olivetti <[email protected]>
Subject: Re: [pymvpa] time complexity of IterativeRelief feature
selection To: Development and support of PyMVPA
<[email protected]> Message-ID:
<[email protected]> Content-Type: text/plain;
charset=ISO-8859-1; format=flowed On 01/12/2011 01:42 PM, Brian Murphy
wrote:
>
> I've been running an IterativeRelief feature selection for five days now, and it still
> hasn't completed. Does anyone have experience to help me get a ball-park estimate of how
> long it should take? My dataset is ~300 samples by ~25,000 features. I see on the API
> documentation that the algorithm has complexity "O(T*N2*I), where T is the number of
> iterations, N the number of instances, I the number of features". Any idea what the
> number of iterations should/might be? I don't see a parameter to set this,
>
Hi Brian,
My guess is that at least one of the following situations occurs:
- The initial guess you are starting from is not good for your
problem. Did you normalize data?
- The threshold you are using is too low and so it take ages, maybe
without any real gain. Try to increase it. Again data nomalization
should play an important role.
- Your problem is not so friendly towards optimization :-) so a
stochastic gradient strategy like IterativeReliefOnline might help.
In any case I strongly suggest you to enable the debug mode
and observe the evolution of the convergence statistics. It will
tell you/us more on where is the problem.
Emanuele
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