Relevance and Kernel Self-Organising Maps
We review the recently proposed method of Kernel Self-organising maps (KSOM) which has been shown to exhibit very fast convergence. We show that this is due to an interaction between the fact that we are working in an overcomplete basis and the fact that we are using a mixture of one-shot and incremental learning. We then review Relevance Vector Machines which is a supervised training method related to Support Vector Machines and apply it to creating the Relevance Self-Organising Map, RSOM. We show results on artificial data and on the iris data set.