Recovery of High-Dimensional Sparse Signals via -Minimization
We consider the recovery of high-dimensional sparse signals via -minimization under mutual incoherence condition, which is shown to be sufficient for sparse signals recovery in the noiseless and noise cases. We study both -minimization under the constraint and the Dantzig selector. Using the two -m...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/636094 |
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| _version_ | 1832564713059254272 |
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| author | Shiqing Wang Limin Su |
| author_facet | Shiqing Wang Limin Su |
| author_sort | Shiqing Wang |
| collection | DOAJ |
| description | We consider the recovery of high-dimensional sparse signals via -minimization under mutual incoherence condition, which is shown to be sufficient for sparse signals recovery in the noiseless and noise cases. We study both -minimization under the constraint and the Dantzig selector. Using the two -minimization methods and a technical inequality, some results are obtained. They improve the results of the error bounds in the literature and are extended to the general case of reconstructing an arbitrary signal. |
| format | Article |
| id | doaj-art-d7277051fe87479d979df12cbc417d3d |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-d7277051fe87479d979df12cbc417d3d2025-02-03T01:10:24ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/636094636094Recovery of High-Dimensional Sparse Signals via -MinimizationShiqing Wang0Limin Su1College of Mathematics and Information Sciences, North China University of Water Resources and Electric Power, Zhengzhou 450011, ChinaCollege of Mathematics and Information Sciences, North China University of Water Resources and Electric Power, Zhengzhou 450011, ChinaWe consider the recovery of high-dimensional sparse signals via -minimization under mutual incoherence condition, which is shown to be sufficient for sparse signals recovery in the noiseless and noise cases. We study both -minimization under the constraint and the Dantzig selector. Using the two -minimization methods and a technical inequality, some results are obtained. They improve the results of the error bounds in the literature and are extended to the general case of reconstructing an arbitrary signal.http://dx.doi.org/10.1155/2013/636094 |
| spellingShingle | Shiqing Wang Limin Su Recovery of High-Dimensional Sparse Signals via -Minimization Journal of Applied Mathematics |
| title | Recovery of High-Dimensional Sparse Signals via -Minimization |
| title_full | Recovery of High-Dimensional Sparse Signals via -Minimization |
| title_fullStr | Recovery of High-Dimensional Sparse Signals via -Minimization |
| title_full_unstemmed | Recovery of High-Dimensional Sparse Signals via -Minimization |
| title_short | Recovery of High-Dimensional Sparse Signals via -Minimization |
| title_sort | recovery of high dimensional sparse signals via minimization |
| url | http://dx.doi.org/10.1155/2013/636094 |
| work_keys_str_mv | AT shiqingwang recoveryofhighdimensionalsparsesignalsviaminimization AT liminsu recoveryofhighdimensionalsparsesignalsviaminimization |