# How to deal with an SVM with categorical attributes

I have a space of 35 dimensions (attributes). My analytic problem is a simple classification one.

Out of 35 dimensions, more than 25 are categorical and each attribute takes more than 50+ types of values.

In that scenario, introducing a dummy variable also will not work for me.

How can I run an SVM on a space which has a lot of categorical attributes?

2. If not, use some coding trick to turn it into numerical attribute. According to the suggestion by the author of libsvm, one can simply use 1-of-K coding. For instance, suppose a 1-dimensional category attribute taking value from $\{A,B,C\}$. Just turn it into 3-dimensional numbers such that $A = (1,0,0)$, $B = (0,1,0)$, $C = (0,0,1)$. Of course, this will incur significantly additional dimensions in your problem, but I think that is not a serious problem for modern SVM solver (no matter Linear type or Kernel type you adopt).