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Compressed sensing aims to undersample certain high-dimensional signals, yet accurately reconstruct them by exploiting signal characteristics.
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S. Sarvotham, D. Baron and R. Baraniuk, “Compressed sensing reconstruction via belief propagation,” Preprint, 2006
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J. A. Tropp, A. C. Gilbert,“ Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Transactions Information Theory
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D. L. Donoho, J. Tanner, “Counting faces of randomly projected hypercubes and orthants, with applications,” ArXiv
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M. J. Wainwright, T. S. Jaakkola and A. S. Willsky,“MAP estimation via agreement on trees: message-passing and linear programming,” IEEE Transactions on Information Theory,
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D. L. Donoho, J. Tanner, “Phase transition as sparse sampling theorems,” IEEE Transactions on Information Theory
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2009
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D. L. Donoho, J. Tanner, “Counting faces of randomly projected polytopes when the projection radically lowers dimension,” J. Amer. Math. Soc
2009
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D. L. Donoho, J.Tanner, “Observed universality of phase transitions in high dimensional geometry, with implication for modern data analysis and signal processing,” Phil. Trans. A, 2009
2009
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