Reduction of kernel models

Kernel models can be expensive to compute and in a non-stationary environment can become unmanageably large. Here we present several previously reported techniques for reducing the complexity of these models in a common framework. This reformulation leads to the development of further related reduction techniques and clarifies the relationships between these and the existing techniques