It seems many kernels for Gaussian processes use magnitudes of input vectors, such as the RBF. If my inputs are age, weight (in grams), height (in mm), and my output is annual salary, then it would seem that height and weight would dominate my kernel, whereas one would expect age to play a more significant role. Is there a way to get around this issue? Most Gaussian process algorithms I have seen don't have a weighting parameter in them.
I would scale/center the features just as in a cluster analysis, e.g. kmeans. The distance between all features is equal after scaling, so no feature would dominate in your kernel/SVM. In theory it should help and it is in general the same process, because in cluster analysis unscaled features face the same problem, because the algorithm would not bother about e.g. the x-axis, if the x-axis ranges from 0-5 and the y-axis from 1000-60000. With scaling the algorithm would no longer be distorted, by higher scales just like your weight or height.
This SO post states exactly as you say that the rbf can have some problems with higher scales of some features. Feature scaling in svm: Does it depend on the Kernel?
So I would first try to scale my features. The question is, if you already did that, and still have those problems. I hope that answers your question, if I understand it right.