Abstract
This paper studies the recovery of the support of sparse signal that is corrupted by both dense noise and gross error. The gross error is an unknown sparse vector whose nonzero entries maybe unbounded. This setup covers a wide range of applications, such as face recognition, inpainting and sensor networks. We derive the information-theoretic lower bounds on the sampling rate required to obtain a desired error rate, which depend on the properties of both the signal and the gross error. The investigations are given in the high-dimensional setting. Some illustrations are provided to further reveal the relationship of these bounds.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: AEUE - International Journal of Electronics and Communications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
