Corpus ID: 12816905

Recoverability of Group Sparse Signals from Corrupted Measurements via Robust Group Lasso

  title={Recoverability of Group Sparse Signals from Corrupted Measurements via Robust Group Lasso},
  author={Xiaohan Wei and Qing Ling and Zhu Han},
This paper considers the problem of recovering a group sparse signal matrix $\mathbf{Y} = [\mathbf{y}_1, \cdots, \mathbf{y}_L]$ from sparsely corrupted measurements $\mathbf{M} = [\mathbf{A}_{(1)}\mathbf{y}_{1}, \cdots, \mathbf{A}_{(L)}\mathbf{y}_{L}] + \mathbf{S}$, where $\mathbf{A}_{(i)}$'s are known sensing matrices and $\mathbf{S}$ is an unknown sparse error matrix. A robust group lasso (RGL) model is proposed to recover $\mathbf{Y}$ and $\mathbf{S}$ through simultaneously minimizing the… Expand
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Dense Error Correction Via $\ell^1$-Minimization
  • John Wright, Y. Ma
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • 2009
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The significance of the results presented in this paper lies in the fact that making explicit use of block-sparsity can provably yield better reconstruction properties than treating the signal as being sparse in the conventional sense, thereby ignoring the additional structure in the problem. Expand