Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Noisy Sparse Recovery Based on Parameterized Quadratic Programming by Thresholding Jun Zhang,1, 2 Yuanqing Li,1 Zhuliang Yu,1 and | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 528734 7 pages doi 2011 528734 Research Article Noisy Sparse Recovery Based on Parameterized Quadratic Programming by Thresholding Jun Zhang 1 2 Yuanqing Li 1 Zhuliang Yu 1 and Zhenghui Gu1 1 Center for Brain-Computer Interfaces and Brain Information Processing College of Automation Science and Engineering South China University of Technology Guangzhou 510640 China 2 College of Information Engineering Guangdong University of Technology Guangzhou 510006 China Correspondence should be addressed to Jun Zhang zhangjun7907@ Received 27 August 2010 Revised 12 December 2010 Accepted 28 January 2011 Academic Editor Walter Kellermann Copyright 2011 Jun Zhang et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Parameterized quadratic programming Lasso is a powerful tool for the recovery of sparse signals based on underdetermined observations contaminated by noise. In this paper we study the problem of simultaneous sparsity pattern recovery and approximation recovery based on the Lasso. An extended Lasso method is proposed with the following main contributions 1 we analyze the recovery accuracy of Lasso under the condition of guaranteeing the recovery of nonzero entries positions. Specifically an upper bound of the tuning parameter h of Lasso is derived. If h exceeds this bound the recovery error will increase with h 2 an extended Lasso algorithm is developed by choosing the tuning parameter according to the bound and at the same time deriving a threshold to recover zero entries from the output of the Lasso. The simulation results validate that our method produces higher probability of sparsity pattern recovery and better approximation recovery compared to two state-of-the-art Lasso methods. .
