Online denoising of discrete noisy data

Pejman Khadivi, Ravi Tandon, Naren Ramakrishnan

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

3 Scopus citations


Real-time data-driven systems often utilize discrete valued time series data and their functionality is highly dependent on the accuracy of such data. In order to improve the performance of these systems, an important pre-processing step is the denoising of data before performing any action (e.g. forecasting or control activities). Existing algorithms have primarily focused on the offline denoising problem, which requires the entire data to be collected before the denoising process. In this paper, the problem of online discrete denoising is considered. The online denoising problem is motivated by real-time applications, where the data must be utilizable soon after it is collected. Three online denoising algorithms are proposed which can strike a tradeoff between delay and accuracy of denoising. It is also shown that the proposed online algorithms asymptotically converge to a class of optimal offline block denoisers.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781467377041
StatePublished - Sep 28 2015
Externally publishedYes
EventIEEE International Symposium on Information Theory, ISIT 2015 - Hong Kong, Hong Kong
Duration: Jun 14 2015Jun 19 2015

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


OtherIEEE International Symposium on Information Theory, ISIT 2015
Country/TerritoryHong Kong
CityHong Kong


  • Discrete Denoising
  • Online Denoising

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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