Big data sketching with model mismatch

Sundeep Prabhakar Chepuri, Yu Zhangt, Geert Leus, Georgios B Giannakis

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

1 Scopus citations

Abstract

Data reduction for large-scale linear regression is one of the most important tasks in this era of data deluge. Exact model information is however not often available for big data analytics. Therefore, we propose a framework for big data sketching (i.e., a data reduction tool) that is robust to possible model mismatch. Such a sketching task is cast as a Boolean min-max optimization problem, and then equivalently reduced to a Boolean minimization program. Capitalizing on the block coordinate descent algorithm, a scalable solver is developed to yield an efficient sampler and a good estimate of the unknown regression coefficient.

Original languageEnglish (US)
Title of host publicationConference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages97-101
Number of pages5
ISBN (Electronic)9781467385763
DOIs
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2016-February
ISSN (Print)1058-6393

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/TerritoryUnited States
CityPacific Grove
Period11/8/1511/11/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Big data
  • data reduction
  • linear regression
  • model mismatch
  • sketching

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