Kernel methods on Categorical Data I have a basic understanding of kernel methods and the kernel-trick and the advantages of it, why it is preferred over conventional machine learning algorithms etc. However, I have some trouble using them.
The problems I face are as follows, 
1. can I use a kernel metric for (dissimilarity) calculation?
2. what steps need to be taken for using a kernel method (say, using the gaussian kernel) on a set of categorical (along with numerical) data. consider the following sample data
    Age     State    Day
12      NJ       Tue 
    24      NM       Wed
    89      CA       Thu
    .       .         .
    .       .         . 
The question is do I need to explicitly encode the categorical values in order to use it on the gaussian kernel for similarity calculation?  
 A: You have several options, the best one being using a kernel function that is tailored to your specific problem. This is also the least intuitive option, so I won't elaborate on that.
Usually, categorical data are encoded using so-called one-hot encoding. This means we introduce one binary feature for every category. For instance, assume we have 3 categories $A$, $B$ and $C$:
$$
A \rightarrow [1, 0 ,0],\quad B\rightarrow[0,1,0],\quad C\rightarrow[0,0,1]
$$
The crucial thing is that we must ensure that all pairwise distances are equal, otherwise the distance will be biased towards some pairs (unless, ofcourse, this is what you want). One-hot encoding is the simplest way to ensure that all pairwise distances between categories are equal.
A: *

*No, by definition, kernel is some kind of similarity not dissimilarity.

*Basically, kernel is a way to express your domain knowledge of the data. For discrete values, you'd better use a kernel that is suitable for the data. Always using Gaussian kernel no matter what data you have is a bad practice.
BTW, kernel methods themselves do not make much sense.
