USPACOR: Universal sparsity-controlling outlier rejection

Georgios B Giannakis, G. Mateos, S. Farahmand, Vasileios Kekatos, H. Zhu

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

46 Scopus citations

Abstract

The recent upsurge of research toward compressive sampling and parsimonious signal representations hinges on signals being sparse, either naturally, or, after projecting them on a proper basis. The present paper introduces a neat link between sparsity and a fundamental aspect of statistical inference, namely that of robustness against outliers, even when the signals involved are not sparse. It is argued that controlling sparsity of model residuals leads to statistical learning algorithms that are computationally affordable and universally robust to outlier models. Analysis, comparisons, and corroborating simulations focus on robustifying linear regression, but succinct overview of other areas is provided to highlight universality of the novel framework.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages1952-1955
Number of pages4
DOIs
StatePublished - Aug 18 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • Lasso
  • Robustness
  • outlier rejection
  • sparsity

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