Title :
Sample complexity for 1-bit compressed sensing and sparse classification
Author :
Gupta, Ankit ; Nowak, Robert ; Recht, Benjamin
Author_Institution :
Samsung Telecommun. America, Richardson, TX, USA
Abstract :
This paper considers the problem of identifying the support set of a high-dimensional sparse vector, from noise-corrupted 1-bit measurements. We present passive and adaptive algorithms for this problem, both requiring no more than O(d log(D)) measurements to recover the unknown support. The adaptive algorithm has the additional benefit of robustness to the dynamic range of the unknown signal.
Keywords :
computational complexity; encoding; pattern classification; 1-bit compressed sensing; O(d log(D)) measurements; high-dimensional sparse vector; noise-corrupted 1-bit measurements; sample complexity; sparse classification; word length 1 bit; Adaptive algorithm; Compressed sensing; Dynamic range; Electric variables measurement; Gaussian noise; Noise measurement; Noise robustness; Signal processing; Signal to noise ratio; Telecommunication computing;
Conference_Titel :
Information Theory Proceedings (ISIT), 2010 IEEE International Symposium on
Conference_Location :
Austin, TX
Print_ISBN :
978-1-4244-7890-3
Electronic_ISBN :
978-1-4244-7891-0
DOI :
10.1109/ISIT.2010.5513510
