Distributed Support Vector Machine Based on Distributed Loss

Yuefeng Ma, Mengwei Wang

Abstract: Support vector machine (SVM) is a fundamental machine learning method with solid mathematical theory and high effectiveness in many applications. Because distributed datasets are difficult to centralize, SVM is hard to be computed by using traditional algorithms in distributed environment. Meanwhile most of existing distributed SVM methods are suffering in very time-consuming. The dilemma of existing distributed SVM methods has hindered their application in a great deal of domain. In this paper, we focus on the improvement of training efficiency for distributed SVM by proposing a distributed SVM method with distributed loss (namely DL-DSVM). We firstly construct an optimization problem of distributed SVM based on distributed loss. Then, considering constrains in distributed environment, we propose a fast training method to solve the optimization problem based on the local optimal solution. Comprehensive experimental results show that DL-DSVM has an excellent performance in time complexity and robustness, and no significant decline in other aspects.