Abstract
In sparse representation, a novel algorithm based on local competitions is proposed to improve the performance of FOCUSS. As l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> optimized function employed in FOCUSS is a non-convex function, FOCUSS has many local minimum, i.e. FOCUSS often can't get the sparsest solution. To find the sparest representation, a new sparse representation algorithm is proposed, which combines FOCUSS and local competitions. Through implementing competitions between neighboring coefficients in the result of FOCUSS, the new method can overcome the shortcoming of FOCUSS. In the experiment of spectrum estimation, the new algorithm has obtained much better amplitude estimation than FOCUSS. Therefore, the proposed algorithm is an effective sparse representation algorithm.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
