An intriguing article. To look at an RBF kernel as a low pass filter is something novel. It also basically shows why RBF kernels work brilliantly on high dimensional images. Given that your image features generally lie in a continuous domain, an RBF kernel generally can fit smooth solutions and thereby create more relevant separating hyperplanes,especially in case of multiple classes.
My first blog on machine learning is to discuss a pet peeve I have about working in the industry, namely why not to apply an RBF kernel to text classification tasks.
I wrote this as a follow up to a Quora Answer on the subject:
I will eventually re-write this entry once I get better at Latex. For now, refer to
Smola, Scholkopf, and Muller, The connection between regularization operators and support vector kernels http://cbio.ensmp.fr/~jvert/svn/bibli/local/Smola1998connection.pdf
I expand on one point–why not to use Radial Basis Function (RBF) Kernels for Text Classification. I encountered this while a consultant a few years ago eBay, where not one but 3 of the teams (local, German, and Indian) were all doing this, with no success They are were treating a multi-class text classification problem using an SVM with an RBF Kernel. What is worse, they were claiming the RBF calculations…
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A Positronic Brain 