Adaptive censoring for large-scale regressions

Dimitris K. Berberidis, Vassilis Kekatos, Gang Wang, Georgios B Giannakis

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

7 Scopus citations

Abstract

Albeit being in the big data era, a significant percentage of data accrued can be overlooked while maintaining reasonable quality of statistical inference at affordable complexity. By capitalizing on data redundancy, interval censoring is leveraged here to cope with the scarcity of resources needed for data exchanging, storing, and processing. By appropriately modifying least-squares regression, first- and second-order algorithms with complementary strengths that operate on censored data are developed for large-scale regressions. Theoretical analysis and simulated tests corroborate their efficacy relative to contemporary competing alternatives.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5475-5479
Number of pages5
Volume2015-August
ISBN (Electronic)9781467369978
DOIs
StatePublished - Jan 1 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: Apr 19 2014Apr 24 2014

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period4/19/144/24/14

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