Large-Scale RLSC Learning Without Agony

Abstract

The advances in kernel-based learning necessitate the study on solving a large-scale non-sparse positive definite linear system. To provide a deterministic approach, recent researches focus on designing fast matrixvector multiplication techniques coupled with a conjugate gradient method. Instead of using the conjugate gradient method, our paper proposes to use a domain decomposition approach in solving such a linear system. Its convergence property and speed can be understood within von Neumann's alternating pro jection framework. We will report significant and consistent improvements in convergence speed over the conjugate gradient method when the approach is applied to recent machine learning problems.