RESEARCH ARTICLE

Signal recovery under mutual incoherence property and oracle inequalities

  • Peng LI 1 ,
  • Wengu CHEN , 2
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  • 1. Graduate School, China Academy of Engineering Physics, Beijing 100088, China
  • 2. Institute of Applied Physics and Computational Mathematics, Beijing 100088, China

Received date: 19 Oct 2017

Accepted date: 13 Oct 2018

Published date: 02 Jan 2019

Copyright

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature

Abstract

We consider the signal recovery through an unconstrained minimiza-tion in the framework of mutual incoherence property. A sufficient condition is provided to guarantee the stable recovery in the noisy case. Furthermore, oracle inequalities of both sparse signals and non-sparse signals are derived under the mutual incoherence condition in the case of Gaussian noises. Finally, we investigate the relationship between mutual incoherence property and robust null space property and find that robust null space property can be deduced from the mutual incoherence property.

Cite this article

Peng LI , Wengu CHEN . Signal recovery under mutual incoherence property and oracle inequalities[J]. Frontiers of Mathematics in China, 2018 , 13(6) : 1369 -1396 . DOI: 10.1007/s11464-018-0733-9

1
Bickel P J, Ritov Y, Tsybakov A B. Simultaneous analysis of Lasso and Dantzig selector. Ann Statist, 2009, 37: 1705–1732

2
Cai T T, Wang L, Xu G. Stable recovery of sparse signals and an oracle inequality. IEEE Trans Inform Theory, 2010, 56: 3516–3522

3
Cai T T, Xu G, Zhang J. On recovery of sparse signals via l1 minimization. IEEE Trans Inform Theory, 2009, 55: 3388–3397

4
Cai T T, Zhang A. Compressed sensing and affine rank minimization under restricted isometry. IEEE Trans Inform Theory, 2013, 61: 3279–3290

5
Cai T T,Zhang A. Sparse representation of a polytope and recovery of sparse signals and low-rank matrices. IEEE Trans Inform Theory, 2014, 60: 122–132

6
Candès E J, Plan Y. Tight oracle inequalities for low-rank matrix recovery from a minimal number of noisy random measurements. IEEE Trans Inform Theory, 2011, 57: 2342–2359

7
Candès E J, Romberg J K, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Comm Pure Appl Math, 2006, 59: 1207–1223

8
Candès E J, Tao T. Decoding by linear programming. IEEE Trans Inform Theory, 2005, 51: 4203–4215

9
Candès E J, Tao T. Near optimal signal recovery from random projections: universal encoding strategies? IEEE Trans Inform Theory, 2006, 52: 5406–5425

10
Candès E J, Tao T. The Dantzig selector: Statistical estimation when p is much larger than n: Ann Statist, 2007, 33: 2313–2351

11
Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM J Sci Comput, 1998, 20: 33–61

12
Cohen A, Dahmen W, DeVore R. Compressed sensing and best k-term approximation. J Amer Math Soc, 2009, 22: 211–231

13
De Castro Y. A remark on the Lasso and the Dantzig selector. Statist Probab Lett, 2013, 83: 304–314

14
Donoho D L. Compressed sensing. IEEE Trans Inform Theory, 2006, 52: 1289–1306

15
Donoho D L, Elad M. Optimally sparse representations in general (nonorthogonal) dictionaries via l1 minimization. Proc Natl Acad Sci USA, 2003, 100: 2197–2202

16
Donoho D L, Elad M, Temlyakov V N. Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans Inform Theory, 2005, 52: 6–18

17
Donoho D L, Huo X. Uncertainty principles and ideal atomic decomposition. IEEE Trans Inform Theory, 2001, 47: 2845–2862

18
Donoho D L, Johnstone I M. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 1994, 81: 425–455

19
Elad M, Milanfar P, Rubinstein R. Analysis versus synthesis in signal priors. Inverse Problems, 2007, 23: 947–968

20
Erickson S, Sabatti C. Empirical Bayes estimation of a sparse vector of gene expression changes. Stat Appl Genet Mol Biol, 2005, 4: 1–27

21
Foucart S. Stability and robustness of l1-minimizations with Weibull matrices and redundant dictionaries. Linear Algebra Appl, 2014, 441: 4–21

22
Fourcat S. Flavors of compressive sensing. In: Fasshauer G E, Schumaker L L, eds. International Conference Approximation Theory, Approximation Theory XV, San Antonio. 2016, 61–104

23
Foucart S, Rauhut H. A Mathematical Introduction to Compressive Sensing, Applied and Numerical Harmonic Analysis Series. New York: Birkhauser, 2013

24
Herman M, Strohmer T. High-resolution radar via compressed sensing. IEEE Trans. Signal Process., 2009, 57: 2275–2284

25
Li P, Chen W G. Signal recovery under cumulative coherence. J Comput Appl Math, 2019, 346: 399–417

26
Lin J H, Li S. Sparse recovery with coherent tight frames via analysis Dantzig selector and analysis LASSO. Appl Comput Harmon Anal, 2014, 37: 126–139

27
Lustig M. Donoho D L, Pauly J M. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine, 2007, 58: 1182–1195

28
Parvaresh F, Vikalo H, Misra S, Hassibi B. Recovering sparse signals using sparse measurement matrices in compressed DNA microarrays. IEEE J Sel Top Signal Process, 2008, 2: 275–285

29
Schnass K, Vandergheynst P. Dictionary preconditioning for greedy algorithms. IEEE Trans Signal Process, 2008, 56: 1994–2002

30
Shen Y, Han B, Braverman E. Stable recovery of analysis based approaches. Appl Comput Harmon Anal, 2015, 39: 161–172

31
Sun Q. Sparse approximation property and stable recovery of sparse signals from noisy measurements. IEEE Trans Signal Process, 2011, 59: 5086–5090

32
Tan Z, Eldar Y C, Beck A, Nehorai A. Smoothing and decomposition for analysis sparse recovery. IEEE Trans Signal Process, 2014, 62: 1762–1774

33
Tauböck G, Hlawatsch F, Eiwen D, Rauhut H. Compressive estimation of doubly selective channels in multicarrier systems: leakage effects and sparsity-enhancing processing. IEEE J Sel Top Signal Process, 2010, 4: 255–271

34
Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B, 1996, 58: 267–288

35
Tropp J A. Greed is good: algorithmic results for sparse approximation. IEEE Trans Inform Theory, 2004, 50: 2231–2242

36
Tseng P. Further results on a stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans Inform Theory, 2009, 55: 888–899

37
Vasanawala S, Alley M, Hargreaves B, Barth R, Pauly J, Lustig M. Improved pediatric MR imaging with compressed sensing. Radiology, 2010, 256: 607–616

38
Wojtaszczyk P. Stability and instance optimality for Gaussian measurements in compressed sensing. Found Comput Math, 2010, 10: 1–13

39
Xia Y, Li S. Analysis recovery with coherent frames and correlated measurements. IEEE Trans Inform Theory, 2016, 62: 6493–6507

40
Zhang H, Yan M, Yin W. One condition for solution uniqueness and robustness of both l1-synthesis and l1-analysis minimizations. Adv Comput Math, 2016, 42: 1381–1399

41
Zhang R, Li S. A proof of conjecture on restricted isometry property constants δtk (0<t<4=3): IEEE Trans Inform Theory, 2018, 65: 1699–1705

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