Distributed Block-diagonal Approximation Methods for Regularized Empirical Risk Minimization

Ching-pei Lee, Kai-Wei Chang

Published 2017-09-10Version 1

In recent years, designing distributed optimization algorithms for empirical risk minimization (ERM) has become an active research topic, mainly because of the practical need to deal with the huge volume of data. In this paper, we propose a general framework for training an ERM model by solving its dual problem in parallel over multiple machines. Viewed as special cases of our framework, several existing methods can be better understood. Our method provides a versatile approach for many large-scale machine learning problems, including linear binary/multi- class classification, regression, and structured prediction. We show that our method, compared with existing approaches, enjoys global linear convergence for a broader class of problems and achieves faster empirical performance.