2013/02/21 by Youssef Mroueh, Lorenzo Rosasco, Mroueh, Youssef +1
Computer Science · Engineering · Mathematics · #Blind Source Separation Techniques #Microwave Imaging and Scattering Analysis #Sparse and Compressive Sensing Techniques #cs.IT #math.IT #math.ST #stat.TH
paper · pdf · doi:10.48550/arxiv.1302.5168
arxiv created 2013/02/21 · arxiv updated 2013/02/22
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.