@inproceedings{661bf1fc9949453eb39b9234bebd75f7,
title = "Minimum entropy pursuit: Noise analysis",
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.",
keywords = "Universal coding, compressed sensing, information dimension, mixing processes",
author = "Shirin Jalali and Poor, {H. Vincent}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7953328",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6100--6104",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
address = "United States",
}