@article{ yang_fast_2013,
  title = {Fast {L}1-{Minimization} {Algorithms} for {Robust} {Face} {Recognition}},
  volume = {22},
  issn = {1057-7149},
  doi = {10.1109/TIP.2013.2262292},
  abstract = {l 1-minimization refers to finding the minimum l1-norm solution to an underdetermined linear system mbib=Ambix. Under certain conditions as described in compressive sensing theory, the minimum l1-norm solution is also the sparsest solution. In this paper, we study the speed and scalability of its algorithms. In particular, we focus on the numerical implementation of a sparsity-based classification framework in robust face recognition, where sparse representation is sought to recover human identities from high-dimensional facial images that may be corrupted by illumination, facial disguise, and pose variation. Although the underlying numerical problem is a linear program, traditional algorithms are known to suffer poor scalability for large-scale applications. We investigate a new solution based on a classical convex optimization framework, known as augmented Lagrangian methods. We conduct extensive experiments to validate and compare its performance against several popular l1-minimization solvers, including interior-point method, Homotopy, FISTA, SESOP-PCD, approximate message passing, and TFOCS. To aid peer evaluation, the code for all the algorithms has been made publicly available.},
  number = {8},
  journal = {IEEE Transactions on Image Processing},
  author = {Yang, A.Y. and Zhou, Zihan and Balasubramanian, A.G. and Sastry, S.S. and Ma, Yi},
  month = {August},
  year = {2013},
  note = {00000},
  pages = {3234--3246}
}

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