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Frontiers of Mathematics in China

ISSN 1673-3452

ISSN 1673-3576(Online)

CN 11-5739/O1

Postal Subscription Code 80-964

2018 Impact Factor: 0.565

Front. Math. China    2018, Vol. 13 Issue (6) : 1369-1396    https://doi.org/10.1007/s11464-018-0733-9
RESEARCH ARTICLE
Signal recovery under mutual incoherence property and oracle inequalities
Peng LI1, Wengu CHEN2()
1. Graduate School, China Academy of Engineering Physics, Beijing 100088, China
2. Institute of Applied Physics and Computational Mathematics, Beijing 100088, China
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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.

Keywords Mutual incoherence property (MIP)      Lasso      Dantzig selector      oracle inequality      robust null space property (RNSP)     
Corresponding Author(s): Wengu CHEN   
Issue Date: 02 January 2019
 Cite this article:   
Peng LI,Wengu CHEN. Signal recovery under mutual incoherence property and oracle inequalities[J]. Front. Math. China, 2018, 13(6): 1369-1396.
 URL:  
https://academic.hep.com.cn/fmc/EN/10.1007/s11464-018-0733-9
https://academic.hep.com.cn/fmc/EN/Y2018/V13/I6/1369
1 P JBickel, YRitov, A BTsybakov. Simultaneous analysis of Lasso and Dantzig selector. Ann Statist, 2009, 37: 1705–1732
2 T TCai, LWang, GXu. Stable recovery of sparse signals and an oracle inequality. IEEE Trans Inform Theory, 2010, 56: 3516–3522
3 T TCai, GXu, JZhang. On recovery of sparse signals via l1 minimization. IEEE Trans Inform Theory, 2009, 55: 3388–3397
4 T TCai, AZhang. Compressed sensing and affine rank minimization under restricted isometry. IEEE Trans Inform Theory, 2013, 61: 3279–3290
5 T T,Cai AZhang. Sparse representation of a polytope and recovery of sparse signals and low-rank matrices. IEEE Trans Inform Theory, 2014, 60: 122–132
6 E JCandès, Y Plan. 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 E JCandès, J K Romberg, TTao. Stable signal recovery from incomplete and inaccurate measurements. Comm Pure Appl Math, 2006, 59: 1207–1223
8 E JCandès, T Tao. Decoding by linear programming. IEEE Trans Inform Theory, 2005, 51: 4203–4215
9 E JCandès, T Tao. Near optimal signal recovery from random projections: universal encoding strategies? IEEE Trans Inform Theory, 2006, 52: 5406–5425
10 E JCandès, T Tao. The Dantzig selector: Statistical estimation when p is much larger than n: Ann Statist, 2007, 33: 2313–2351
11 S SChen, D LDonoho, M ASaunders. Atomic decomposition by basis pursuit. SIAM J Sci Comput, 1998, 20: 33–61
12 ACohen, WDahmen, RDeVore. Compressed sensing and best k-term approximation. J Amer Math Soc, 2009, 22: 211–231
13 YDe Castro. A remark on the Lasso and the Dantzig selector. Statist Probab Lett, 2013, 83: 304–314
14 D LDonoho. Compressed sensing. IEEE Trans Inform Theory, 2006, 52: 1289–1306
15 D LDonoho, MElad. Optimally sparse representations in general (nonorthogonal) dictionaries via l1 minimization. Proc Natl Acad Sci USA, 2003, 100: 2197–2202
16 D LDonoho, MElad, V NTemlyakov. Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans Inform Theory, 2005, 52: 6–18
17 D LDonoho, XHuo. Uncertainty principles and ideal atomic decomposition. IEEE Trans Inform Theory, 2001, 47: 2845–2862
18 D LDonoho, I M Johnstone. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 1994, 81: 425–455
19 MElad, P Milanfar, RRubinstein. Analysis versus synthesis in signal priors. Inverse Problems, 2007, 23: 947–968
20 SErickson, C Sabatti. Empirical Bayes estimation of a sparse vector of gene expression changes. Stat Appl Genet Mol Biol, 2005, 4: 1–27
21 SFoucart. Stability and robustness of l1-minimizations with Weibull matrices and redundant dictionaries. Linear Algebra Appl, 2014, 441: 4–21
22 SFourcat. 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 SFoucart, HRauhut. A Mathematical Introduction to Compressive Sensing, Applied and Numerical Harmonic Analysis Series. New York: Birkhauser, 2013
24 MHerman, T Strohmer. High-resolution radar via compressed sensing. IEEE Trans. Signal Process., 2009, 57: 2275–2284
25 PLi, W GChen. Signal recovery under cumulative coherence. J Comput Appl Math, 2019, 346: 399–417
26 J HLin, SLi. Sparse recovery with coherent tight frames via analysis Dantzig selector and analysis LASSO. Appl Comput Harmon Anal, 2014, 37: 126–139
27 MLustig. D LDonoho, J MPauly. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine, 2007, 58: 1182–1195
28 FParvaresh, HVikalo, SMisra, B Hassibi. Recovering sparse signals using sparse measurement matrices in compressed DNA microarrays. IEEE J Sel Top Signal Process, 2008, 2: 275–285
29 KSchnass, P Vandergheynst. Dictionary preconditioning for greedy algorithms. IEEE Trans Signal Process, 2008, 56: 1994–2002
30 YShen, BHan, EBraverman. Stable recovery of analysis based approaches. Appl Comput Harmon Anal, 2015, 39: 161–172
31 QSun. Sparse approximation property and stable recovery of sparse signals from noisy measurements. IEEE Trans Signal Process, 2011, 59: 5086–5090
32 ZTan, Y CEldar, ABeck, A Nehorai. Smoothing and decomposition for analysis sparse recovery. IEEE Trans Signal Process, 2014, 62: 1762–1774
33 GTauböck, F Hlawatsch, DEiwen, HRauhut. 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 RTibshirani. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B, 1996, 58: 267–288
35 J ATropp. Greed is good: algorithmic results for sparse approximation. IEEE Trans Inform Theory, 2004, 50: 2231–2242
36 PTseng. Further results on a stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans Inform Theory, 2009, 55: 888–899
37 SVasanawala, MAlley, BHargreaves, RBarth, JPauly, MLustig. Improved pediatric MR imaging with compressed sensing. Radiology, 2010, 256: 607–616
38 PWojtaszczyk. Stability and instance optimality for Gaussian measurements in compressed sensing. Found Comput Math, 2010, 10: 1–13
39 YXia, SLi. Analysis recovery with coherent frames and correlated measurements. IEEE Trans Inform Theory, 2016, 62: 6493–6507
40 HZhang, MYan, WYin. One condition for solution uniqueness and robustness of both l1-synthesis and l1-analysis minimizations. Adv Comput Math, 2016, 42: 1381–1399
41 RZhang, SLi. 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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