[R] data structure problem

[Liaw, Andy]

If the data somewhat resembles multivariate Gaussian, I suppose one
possibility is to construct (by hand) something like LDA, but with the
covariance matrix constrained to be block-diagonal.  Just an idea.

Cheers,
Andy


> From: Jieping [mailto:jzhao at unity.ncsu.edu] 
> 
> my situtation is that each data point is made up of p 
> correlated 5-dimension
> vectors. Those 5 dimensions are orthogonal.
> Any suggestions will be appreciated!
> 
> JP
>
> From: Liaw, Andy [mailto:andy_liaw at merck.com]
> 
> Without more information on the context of the data, it's 
> hard to say much
> that will be useful.
> 
> One possibility is to treat the 5*p entries as 5*p variables, 
> and apply the
> commonly available discriminant tools to that.  Given more 
> information, it
> might be possible to do better.  As an example, one data set 
> that has been
> used as benchmark is the scanned images of hand-written 
> digits.  Each digit
> is encoded in a k x k matrix of values expressing the 
> grayscale level of
> each pixel (don't remember what k is).  A straight-forward 
> way to train a
> algorithm for pattern recognition is to treat the data as having kxk
> variables.  However, smarter (but custom-built, rather than 
> off-the-shelf)
> algorithms can make use of the fact that the data is actually 
> an image, and
> possibly get better results.
> 
> Cheers,
> Andy
> 
> > From: Jieping
> >
> > HI, there,
> >    I have a data set with special structure.
> >    It is in n*(5*p): n is the number of observations or data points
> >                      5*p is the matrix for each data point
> >    I'd like to conduct discriminant analysis to this data
> > set. How could I
> > do? And where could I find related references to solve this problem?
> >
> > Thanks a lot!
> >
> >
> > Jieping Zhao
> > PhD student in Bioinformatics, NCSU
> > Lab homepage: http://coltrane.gnets.ncsu.edu/index.html
> >
> 
> 
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