The Group Lasso for Stable Recovery of Block-Sparse Signal Representations

@article{Lv2011TheGL,
  title={The Group Lasso for Stable Recovery of Block-Sparse Signal Representations},
  author={Xiaolei Lv and Guoan Bi and Chunru Wan},
  journal={IEEE Transactions on Signal Processing},
  year={2011},
  volume={59},
  pages={1371-1382}
}
Group Lasso is a mixed l1/l2-regularization method for a block-wise sparse model that has attracted a lot of interests in statistics, machine learning, and data mining. This paper establishes the possibility of stably recovering original signals from the noisy data using the adaptive group Lasso with a combination of sufficient block-sparsity and favorable block structure of the overcomplete dictionary. The corresponding theoretical results about the solution uniqueness, support recovery and… CONTINUE READING
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