Suppose that we have an array of
10x2 elements (features). Each of these features are two-dimensional. Something like this:
Array A: 0.001 0.56 0.045 0.12 0.546 0.54 0.123 0.12 1.435 0.01 1.234 0.01 . . . 1.654 0.12
Now, if I want to project this feature space in a higher dimension I need to implement a Kernel. Consider the linear kernel: $K(x,y) = (x' \times y +1)$.
I am confused about this $x$ and $y$. What are my $x$ and $y$ here? Do I need more data? These features are also supposed to belong in two different categories. Do I have to split array A into two different arrays each one contains samples from class A and B, and then do the dot products?