Hi there,
I have a statistics question on a classification problem:

Suppose I have 1000 binary variables and one binary dependent variable. I
want to find a way similar to PCA, in which I can find a couple of
combinations of those variables to discriminate best according to the
dependent variable. It is not only for dimension reduction, but more
important, for finding best way to construct features. This is NOT CDA since
the explanatory variables are NOT continuous. I knew the existence of that
method since I consulted before with a professor but I forgot the name of
the method.. sigh...

I am also wondering if R has already some function or package addressing
this kind of problem.

Thanks

--
Weiwei Shi, Ph.D

"Did you always know?"
"No, I did not. But I believed..."
---Matrix III

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