Online censoring for large-scale regressions

D. Berberidis, G. Wang, G. B. Giannakis, V. Kekatos

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

2 Scopus citations


As every day 2.5 quintillion bytes of data are generated, the era of Big Data is undoubtedly upon us. Nonetheless, a significant percentage of the data accrued can be omitted while maintaining a certain quality of statistical inference with a limited computational budget. In this context, estimating adaptively high-dimensional signals from massive data observed sequentially is challenging but equally important in practice. The present paper deals with this challenge based on a novel approach that leverages interval censoring for data reduction. An online maximum likelihood, least mean-square (LMS)-type algorithm, and an online support vector regression algorithm are developed for censored data. The proposed algorithms entail simple, low-complexity, closed-form updates, and have provably bounded regret. Simulated tests corroborate their efficacy.

Original languageEnglish (US)
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479982974
StatePublished - Apr 24 2015
Event48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 2 2014Nov 5 2014

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/TerritoryUnited States
CityPacific Grove


  • D.4. Adaptive Filtering
  • Technical Area


Dive into the research topics of 'Online censoring for large-scale regressions'. Together they form a unique fingerprint.

Cite this