A distributed kernel summation framework for general‐dimension machine learning

摘要

Abstract Kernel summations are a ubiquitous key computational bottleneck in many data analysis methods. In this paper, we attempt to marry, for the first time, the best relevant techniques in parallel computing, where kernel summations are in low dimensions, with the best general-dimension algorithms from the machine learning literature. We provide the first distributed implementation of kernel summation framework that can utilize: (i) various types of deterministic and probabilistic approximat.