Distributed mini-batch random projection algorithms for reduced communication overhead

Soomin Lee, Angelia Nedic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose a gossip-based mini-batch random projection (GMRP) algorithm that can reduce communication overhead for a distributed optimization problem defined over a network with a very large number of constraints. We state a convergence result and provide an application of the GMRP, text classification with support vector machines.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages559-562
Number of pages4
DOIs
StatePublished - Dec 1 2013
Externally publishedYes
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
CountryUnited States
CityAustin, TX
Period12/3/1312/5/13

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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    Lee, S., & Nedic, A. (2013). Distributed mini-batch random projection algorithms for reduced communication overhead. In 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings (pp. 559-562). [6736939] (2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings). https://doi.org/10.1109/GlobalSIP.2013.6736939