Revisiting compressed sensing: exploiting the efficiency of simplex and sparsification methods
Author:
Publisher
Springer Science and Business Media LLC
Subject
Software,Theoretical Computer Science
Link
http://link.springer.com/content/pdf/10.1007/s12532-016-0105-y.pdf
Reference45 articles.
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2. Belloni, A., Chernozhukov, V.: $$\ell _1$$ ℓ 1 -penalized quantile regression in high-dimensional sparse models. Ann. Stat. 39, 82–130 (2011)
3. Cai, T.T., Zhang, A.: Sharp RIP bound for sparse signal and low-rank matrix recovery. Appl. Comput. Harmonic Anal. 35, 74–93 (2012)
4. Candès, E., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52, 489–509 (2006)
5. Candès, E.J.: The restricted isometry property and its implications for compressed sensing. C. R. Math. 346, 589–592 (2008)
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