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:[EMAIL PROTECTED] > > 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:[EMAIL PROTECTED] > > 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 > > > > > -------------------------------------------------------------- > -------------- > -- > Notice: This e-mail message, together with any attachments, contains > information of Merck & Co., Inc. (One Merck Drive, Whitehouse > Station, New > Jersey, USA 08889), and/or its affiliates (which may be known > outside the > United States as Merck Frosst, Merck Sharp & Dohme or MSD) that may be > confidential, proprietary copyrighted and/or legally > privileged, and is > intended solely for the use of the individual or entity named on this > message. > If you are not the intended recipient, and have received this > message in > error, please immediately return this by e-mail and then delete it. > -------------------------------------------------------------- > -------------- > -- > > > > ------------------------------------------------------------------------------ Notice: This e-mail message, together with any attachments,...{{dropped}} ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
