Multi-Scale Nystrom Method

Kernel methods are powerful tools for modeling nonlinear data. However, the amount of computation and memory required for kernel methods becomes the bottleneck when dealing with large-scale problems. In this paper, we propose Nested Nystrom Method (NNM) which achieves a delicate balance between the approximation accuracy and computational efficiency by exploiting the multilayer structure and multiple compressions. Even when the size of the kernel matrix is very large, NNM consistently decomposes very small matrices to update the eigen-decomposition of the kernel matrix. We theoretically show that NNM implicitly updates the principal sub-space through the multiple layers, and also prove that its corresponding errors of rank-k PSD matrix approximation and kernel PCA (KPCA) are decreased by using additional sublayers before the final layer. Finally, we empirically demonstrate the decreasing property of errors of NNM with the additional sublayers through the experiments on the constructed kernel matrices of real data sets, and show that NNM effectively controls the efficiency both for rank-k PSD matrix approximation and KPCA.