[R] data structure problem
Liaw, Andy
andy_liaw at merck.com
Wed Jan 14 14:17:33 CET 2004
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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