Distributed sketched subspace clustering for large-scale datasets

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

Abstract

Subspace clustering has been a successful tool for unsupervised classification of high-dimensional and generally 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 distributed subspace clustering scheme for high-volume data based on random projections. Additionally, the method can cope with corrupted data. Performance of the novel scheme is assessed via numerical tests, and is compared with state-of-the-art subspace clustering methods.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538612514
DOIs
StatePublished - Mar 9 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: Dec 10 2017Dec 13 2017

Publication series

Name2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Volume2017-December

Conference

Conference7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

Bibliographical note

Funding Information:
Work in this paper was supported by NSF grants 1500713 and 1514056.

Publisher Copyright:
© 2017 IEEE.

Keywords

  • Subspace clustering
  • big data
  • distributed
  • missing data
  • random projections
  • sketching

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