MATLAB: How to compute kernels


I want to calculate weighted kernels (for using in a SVM classifier) in Matlab but I'm currently compeletely confused.
I would like to implement the following weighted RBF and Sigmoid kernel:
x and y are vectors of size n, gamma and b are constants and w is a vector of size n with weights.
The problem now is that the fitcsvm method from Matlab need two matrices as input, i.e. K(X,Y). For example the not weighted RBF and sigmoid kernel can be computed as follows:
K_rbf = exp(-gamma .* pdist2(X,Y,'euclidean').^2)
K_sigmoid = tanh(gamma*X*Y' + b);
X and Y are matrices where the rows are the data points (vectors).
How can I compute the above weighted kernels efficiently in Matlab?
Can I just apply the weights before computing the kernel? And if yes, how can this be done?

Best Answer

instead of pdist2, that performs rest sqrt, and then back ^2, you could simply use the operators .^ and .* to operate element wise on W X and Y:
K_rbf = exp(-gamma * sum(sum(W.*((X-Y).^2))))
you don't mention it, but gamma seems to be a scalar, so gamma does not need the element wise product .*
2 sum() because sum only sums along a given dimension.
Why do you write that K(x,y)=exp( .. is same as K(x,y)=tanh( .. ?
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