Online sketching for big data subspace learning

Morteza Mardani, Georgios B Giannakis

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

2 Scopus citations


Sketching (a.k.a. subsampling) high-dimensional data is a crucial task to facilitate data acquisition process e.g., in magnetic resonance imaging, and to render affordable 'Big Data' analytics. Multidimensional nature and the need for realtime processing of data however pose major obstacles. To cope with these challenges, the present paper brings forth a novel real-time sketching scheme that exploits the correlations across data stream to learn a latent subspace based upon tensor PARAFAC decomposition 'on the fly.' Leveraging the online subspace updates, we introduce a notion of importance score, which is subsequently adapted into a randomization scheme to predict a minimal subset of important features to acquire in the next time instant. Preliminary tests with synthetic data corroborate the effectiveness of the novel scheme relative to uniform sampling.

Original languageEnglish (US)
Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9780992862633
StatePublished - Dec 22 2015
Event23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
Duration: Aug 31 2015Sep 4 2015

Publication series

Name2015 23rd European Signal Processing Conference, EUSIPCO 2015


Other23rd European Signal Processing Conference, EUSIPCO 2015

Bibliographical note

Funding Information:
Supported by the MURI Grant No. AFOSR FA9550-10-1-0567


  • Tensor
  • randomization
  • streaming data
  • subspace learning


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