Distributed and Robust Support Vector Machine

Yangwei Liu, Hu Ding, Ziyun Huang, Jinhui Xu

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we consider the distributed version of Support Vector Machine (SVM) under the coordinator model, where all input data (i.e., points in d space) of SVM are arbitrarily distributed among k nodes in some network with a coordinator which can communicate with all nodes. We investigate two variants of this problem, with and without outliers. For distributed SVM without outliers, we prove a lower bound on the communication complexity and give a distributed (1 - )-approximation algorithm to reach this lower bound, where is a user specified small constant. For distributed SVM with outliers, we present a (1 - )-approximation algorithm to explicitly remove the influence of outliers. Our algorithm is based on a deterministic distributed top t selection algorithm with communication complexity of O(klog(t)) in the coordinator model.

Original languageEnglish (US)
Pages (from-to)213-233
Number of pages21
JournalInternational Journal of Computational Geometry and Applications
Volume30
Issue number3-4
DOIs
StatePublished - Sep 2020

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Geometry and Topology
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

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