A randomized approach to large-scale subspace clustering

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

2 Scopus citations

Abstract

Subspace clustering has become a popular tool for clustering high-dimensional non-linearly separable data. However, state-of-the-art subspace clustering algorithms do not scale well as the number of data increases. The present paper puts forth a novel randomized subspace clustering scheme for high-volume data based on random projections. Performance of the proposed method is assessed via numerical tests, and is compared with state-of-the-art subspace clustering and large-scale subspace clustering methods.

Original languageEnglish (US)
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1019-1023
Number of pages5
ISBN (Electronic)9781538639542
DOIs
StatePublished - Mar 1 2017
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: Nov 6 2016Nov 9 2016

Publication series

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

Other

Other50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
CountryUnited States
CityPacific Grove
Period11/6/1611/9/16

Bibliographical note

Funding Information:
Work in this paper was supported by NSF grants 1247885,1343248, 1343860, 1500713, 1514056, and NIH 1R01GM104975-01.

Publisher Copyright:
© 2016 IEEE.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

  • Subspace clustering
  • big data
  • random projections
  • sketching

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