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Sparse Recovery by means of Nonnegative Least Squares

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Abstract
  • This short note demonstrates that sparse recovery can be achieved by an l₁-minimization ersatz easily implemented using a conventional nonnegative least squares algorithm. A connection with orthogonal matching pursuit is also highlighted. The preliminary results call for more investigations on the potential of the method and on its relations to classical sparse recovery algorithms.
  • Keywords: Sparse recovery, Nonnegative least squares, k-mer frequency matrices, Orthogonal matching pursuit, Compressive sensing, Adjacency matrices of bipartite graphs, l₁-minimization, Gaussian matrices
  • (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article can be found at: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=97.
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  • Foucart, S., & Koslicki, D. (2014). Sparse Recovery by Means of Nonnegative Least Squares. IEEE Signal Processing Letters, 21(4), 498-502. doi:10.1109/LSP.2014.2307064
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  • 21
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  • 4
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  • This work is supported by the NSF under grant DMS-1120622.
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