Nonlinear sampling for sparse recovery

2015 ◽  
Author(s):  
Seyed Amir-Hossein Hosseini ◽  
Mahdi Barzegar Khalilsarai ◽  
Arash Amini ◽  
Farokh Marvasti
Keyword(s):  
Sparse Recovery ◽  
Nonlinear Sampling

SegOMP: Sparse Recovery with Fewer Measurements

2014 ◽  
Vol E97.A (3) ◽  
pp. 862-864 ◽  
Author(s):  
Li ZENG ◽  
Xiongwei ZHANG ◽  
Liang CHEN ◽  
Weiwei YANG
Keyword(s):  
Sparse Recovery

Joint Sparse Recovery for Signals of Spark-level Sparsity via MMV Tail-$\ell_{2,1}$ Minimization

2021 ◽  
pp. 1-1
Author(s):  
Baifu Zheng ◽  
Cao Zeng ◽  
Shidong Li ◽  
Guisheng Liao
Keyword(s):  
Sparse Recovery

scholarly journals Quantitative Estimates for Nonlinear Sampling Kantorovich Operators

2021 ◽  
Vol 76 (2) ◽  
Author(s):  
Nursel Çetin ◽  
Danilo Costarelli ◽  
Gianluca Vinti
Keyword(s):  
Orlicz Spaces ◽  
General Setting ◽  
Modulus Of Smoothness ◽  
Direct Approach ◽  
Order Of Approximation ◽  
Lipschitz Classes ◽  
General Frame ◽  
Quantitative Estimates ◽  
Nonlinear Sampling ◽  
Kantorovich Operators

AbstractIn this paper, we establish quantitative estimates for nonlinear sampling Kantorovich operators in terms of the modulus of smoothness in the setting of Orlicz spaces. This general frame allows us to directly deduce some quantitative estimates of approximation in $$L^{p}$$ L p -spaces, $$1\le p<\infty $$ 1 ≤ p < ∞ , and in other well-known instances of Orlicz spaces, such as the Zygmung and the exponential spaces. Further, the qualitative order of approximation has been obtained assuming f in suitable Lipschitz classes. The above estimates achieved in the general setting of Orlicz spaces, have been also improved in the $$L^p$$ L p -case, using a direct approach suitable to this context. At the end, we consider the particular cases of the nonlinear sampling Kantorovich operators constructed by using some special kernels.

Classification of ISAR images using sparse recovery algorithms

2014 ◽  
Author(s):  
Seung-Jae Lee ◽  
Ji-Hoon Bae ◽  
Byung-Soo Kang ◽  
Kyung-Tae Kim ◽  
Eun-Jung Yang
Keyword(s):  
Sparse Recovery ◽  
Recovery Algorithms
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