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  • Research Article
  • Open Access

Noisy Sparse Recovery Based on Parameterized Quadratic Programming by Thresholding

EURASIP Journal on Advances in Signal Processing20112011:528734

  • Received: 27 August 2010
  • Accepted: 28 January 2011
  • Published:


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.


  • Information Technology
  • High Probability
  • Quantum Information
  • Quadratic Program
  • Tuning Parameter

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Authors’ Affiliations

Center for Brain-Computer Interfaces and Brain Information Processing, College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China
College of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China


© Jun Zhang et al. 2011

This article is published under license to BioMed Central Ltd. 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.