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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