Large-scale subspace clustering using random sketching and validation

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

1 Scopus citations

Abstract

While successful in clustering multiple types of high-dimensional data, subspace clustering algorithms do not scale well as the number of data increases. The present paper puts forth a novel randomized subspace clustering algorithm for high-dimensional data based on a random sketching and validation approach. Utilizing a data-driven random sketching technique to estimate the underlying probability density function of the data, the performance of the proposed method is assessed via simulations, and is compared with state-of-the-art sparse subspace clustering methods.

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
Pages107-111
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
CountryUnited States
CityPacific Grove
Period11/8/1511/11/15

Bibliographical note

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

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Subspace clustering
  • big data
  • kernel smoothing
  • random sketching and validation
  • sparsity

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