Online dictionary learning from large-scale binary data

Yanning Shen, Georgios B Giannakis

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

3 Scopus citations

Abstract

Compressive sensing (CS) has been shown useful for reducing dimensionality, by exploiting signal sparsity inherent to specific domain representations of data. Traditional CS approaches represent the signal as a sparse linear combination of basis vectors from a prescribed dictionary. However, it is often impractical to presume accurate knowledge of the basis, which motivates data-driven dictionary learning. Moreover, in large-scale settings one may only afford to acquire quantized measurements, which may arrive sequentially in a streaming fashion. The present paper jointly learns the sparse signal representation and the unknown dictionary when only binary streaming measurements with possible misses are available. To this end, a novel efficient online estimator with closedform sequential updates is put forth to recover the sparse representation, while refining the dictionary 'on the fly'. Numerical tests on simulated and real data corroborate the efficacy of the novel approach.

Original languageEnglish (US)
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1808-1812
Number of pages5
ISBN (Electronic)9780992862657
DOIs
StatePublished - Nov 28 2016
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: Aug 28 2016Sep 2 2016

Publication series

NameEuropean Signal Processing Conference
Volume2016-November
ISSN (Print)2219-5491

Other

Other24th European Signal Processing Conference, EUSIPCO 2016
CountryHungary
CityBudapest
Period8/28/169/2/16

Keywords

  • Binary data
  • Dictionary learning
  • Online learning

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