Minimum entropy pursuit: Noise analysis

Shirin Jalali, H. Vincent Poor

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

1 Scopus citations

Abstract

Universal compressed sensing algorithms recover a 'structured' signal from its under-sampled linear measurements, without knowing its distribution. The recently developed minimum entropy pursuit (MEP) optimization suggests a framework for developing universal compressed sensing algorithms. In the noiseless setting, among all signals that satisfy the measurement constraints, MEP seeks the 'simplest'. In this work, the effect of noise on the performance of the relaxed version of MEP optimization, namely Lagrangian-MEP, is studied. It is proved that the performance the Lagrangian-MEP algorithm is robust to small additive noise.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6100-6104
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Jalali, S., & Poor, H. V. (2017). Minimum entropy pursuit: Noise analysis. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 6100-6104). [7953328] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7953328