Dear Peter Flom,
Some coments about your email:
- When you say "classify ... neurological problems" I think that it is
general and probably there are characteristics (in signal) that can define
which neurological problems, for example are your talkin about spikes
related to schizophrenia?
- It is interesting to do a Principal componene analysis over data, but here
you can obtain : (ortogonal) components ordered by their variances, if you
have a good signal-to-noise-ratio (SNR) I think that there are no problems.
But if you have low SNR, you need to be carefully about the high variance of
the noise compared with the signal of interest and then lost the
characteristics of the rela signal of interes, and when you cluster it can
be appear spread, interfering the clustering.
- When you use PCA probably you have the most correlated cases but not
independent, because uncorrelation not meaning independence.
- If you have "the diagnosis of the people " why didn´t you choose the most
representative (a percentage of the set) and train and algoritm of
classification, after that test with other little percentage, and at the end
you can clasify the rest of data ?
Best Regards,
Carlos Estombelo-Montesco
2007/4/24, Peter Flom <[log in to unmask]>:
>
> Hello
>
> (note that this is the same Peter Flom at a different address with a new
> e-mail and a new job)
>
> I have a data set with about 800 people and about 1000 variables. The
> variables are all 'features' of EEG data that have been extracted by subject
> matter experts in neurology as being potentially useful. All variables have
> been standardized to mean 0, sd 1. There are many high correlations among
> them.
>
> We are interested in many aspects of this data - one primary aim is to use
> the EEG data to better classify people who have neurological problems. Two
> methods that seem particularly relevant to this list are clustering and
> decision trees. I've done a bit of both, but always on data sets with FAR
> fewer variables (e.g. about 10 variables). Especially with regard to
> clustering, I was thinking of doing a principal components analysis prior to
> the cluster analysis (perhaps with SAS PRINCOMP, FACTOR, or VARCLUS).
>
> With regard to trees, I've done some 'basic' analysis of other data sets
> using R's 'party' and 'rpart' packages. With those data sets, however, the
> main goal was explanation, and so, I did not explore bagging and boosting
> and such. Any pointers or introductions to that literature would be most
> welcome (preferably at a not TOO high mathematical level - I had some
> calculus many years ago, but am much more interested in applications than in
> 'theorem-proof' material).
>
> I will be exploring this data set for quite some time, so am willing to
> invest some effort to learn best practices, and am also willing to try a
> variety of methods.
>
> Finally, as to why I am looking at both trees and clusters - partly, we
> know the diagnosis of the people (hence trees are useful) but we also know
> that there are difficulties with the diagnoses, and that these difficulties
> may be amenable to exploration with sophisticated methods
>
>
> Thanks in advance
>
> Peter Flom
> Brainscope, Inc.
>
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+--------------------------------------------------------------------------------------+
Carlos Alberto Estombelo Montesco
PhD. Student in Physics Applied to Medicine and
Biology
.......................................................................................
University of Sao Paulo
Department of Physics and Mathematics
School of Philosophy, Sciences and Letters of Ribeirão Preto
Av. Bandeirantes, 3900 CEP: 14040-901 Ribeirão Preto, SP, Brazil
fax : +55 16 3602 4887
email: [log in to unmask]
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