q-ary Compressive Sensing
Information Theory
2013-02-22 v1 math.IT
Statistics Theory
Statistics Theory
Abstract
We introduce q-ary compressive sensing, an extension of 1-bit compressive sensing. We propose a novel sensing mechanism and a corresponding recovery procedure. The recovery properties of the proposed approach are analyzed both theoretically and empirically. Results in 1-bit compressive sensing are recovered as a special case. Our theoretical results suggest a tradeoff between the quantization parameter q, and the number of measurements m in the control of the error of the resulting recovery algorithm, as well its robustness to noise.
Cite
@article{arxiv.1302.5168,
title = {q-ary Compressive Sensing},
author = {Youssef Mroueh and Lorenzo Rosasco},
journal= {arXiv preprint arXiv:1302.5168},
year = {2013}
}