Weighted and structured sparse total least-squares for perturbed compressive sampling

Hao Zhu, Georgios B Giannakis, Geert Leus

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

7 Scopus citations

Abstract

Solving linear regression problems based on the total least-squares (TLS) criterion has well-documented merits in various applications, where perturbations appear both in the data vector as well as in the regression matrix. Weighted and structured generalizations of the TLS approach are further motivated in several signal processing and system identification related problems. On the other hand, modern compressive sampling and variable selection algorithms account for perturbations of the data vector, but not those affecting the regression matrix. The present paper addresses also the latter by introducing a weighted and structured sparse (S-) TLS formulation to exploit a priori knowledge on both types of perturbations, and on the sparsity of the unknown vector. The resultant novel approach is further able to cope with sparse, under-determined errors-in-variables models with structured and correlated perturbations, while allowing for efficient sub-optimum solvers. Simulated tests demonstrate the approach, and especially its ability to reliably recover the support of unknown sparse vectors.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages3792-3795
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

  • Total least-squares
  • coordinate descent
  • errors-in-variables models
  • sparsity

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    Zhu, H., Giannakis, G. B., & Leus, G. (2011). Weighted and structured sparse total least-squares for perturbed compressive sampling. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings (pp. 3792-3795). [5947177] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2011.5947177