Sparse non-negative signal reconstruction using fraction function penalty
Many practical problems in the real world can be formulated as the non-negative $\ell _0$ℓ0-minimisation problems, which seek the sparsest non-negative signals to underdetermined linear equations. They have been widely applied in signal and image processing, machine learning, pattern recognition and...
Uloženo v:
| Vydáno v: | IET signal processing Ročník 13; číslo 2; s. 125 - 132 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
The Institution of Engineering and Technology
01.04.2019
|
| Témata: | |
| ISSN: | 1751-9675, 1751-9683, 1751-9683 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Many practical problems in the real world can be formulated as the non-negative $\ell _0$ℓ0-minimisation problems, which seek the sparsest non-negative signals to underdetermined linear equations. They have been widely applied in signal and image processing, machine learning, pattern recognition and computer vision. Unfortunately, this non-negative $\ell _0$ℓ0-minimisation problem is non-deterministic polynomial hard (NP-hard) because of the discrete and discontinuous nature of the $\ell _0$ℓ0-norm. Inspired by the good performances of the fraction function in the authors’ former work, in this paper, the authors replace the $\ell _0$ℓ0-norm with the non-convex fraction function and study the minimisation problem of the fraction function in recovering the sparse non-negative signal from an underdetermined linear equation. They discuss the equivalence between non-negative $\ell _0$ℓ0-minimisation problem and non-negative fraction function minimisation problem, and the equivalence between non-negative fraction function minimisation problem and regularised non-negative fraction function minimisation problem. It is proved that the optimal solution to the non-negative $\ell _0$ℓ0-minimisation problem could be approximately obtained by solving their regularised non-negative fraction function minimisation problem if some specific conditions are satisfied. Then, they propose a non-negative iterative thresholding algorithm to solve their regularised non-negative fraction function minimisation problem. At last, numerical experiments on some sparse non-negative signal recovery problems are reported. |
|---|---|
| AbstractList | Many practical problems in the real world can be formulated as the non‐negative ‐minimisation problems, which seek the sparsest non‐negative signals to underdetermined linear equations. They have been widely applied in signal and image processing, machine learning, pattern recognition and computer vision. Unfortunately, this non‐negative ‐minimisation problem is non‐deterministic polynomial hard (NP‐hard) because of the discrete and discontinuous nature of the ‐norm. Inspired by the good performances of the fraction function in the authors’ former work, in this paper, the authors replace the ‐norm with the non‐convex fraction function and study the minimisation problem of the fraction function in recovering the sparse non‐negative signal from an underdetermined linear equation. They discuss the equivalence between non‐negative ‐minimisation problem and non‐negative fraction function minimisation problem, and the equivalence between non‐negative fraction function minimisation problem and regularised non‐negative fraction function minimisation problem. It is proved that the optimal solution to the non‐negative ‐minimisation problem could be approximately obtained by solving their regularised non‐negative fraction function minimisation problem if some specific conditions are satisfied. Then, they propose a non‐negative iterative thresholding algorithm to solve their regularised non‐negative fraction function minimisation problem. At last, numerical experiments on some sparse non‐negative signal recovery problems are reported. Many practical problems in the real world can be formulated as the non-negative $\ell _0$ℓ0-minimisation problems, which seek the sparsest non-negative signals to underdetermined linear equations. They have been widely applied in signal and image processing, machine learning, pattern recognition and computer vision. Unfortunately, this non-negative $\ell _0$ℓ0-minimisation problem is non-deterministic polynomial hard (NP-hard) because of the discrete and discontinuous nature of the $\ell _0$ℓ0-norm. Inspired by the good performances of the fraction function in the authors’ former work, in this paper, the authors replace the $\ell _0$ℓ0-norm with the non-convex fraction function and study the minimisation problem of the fraction function in recovering the sparse non-negative signal from an underdetermined linear equation. They discuss the equivalence between non-negative $\ell _0$ℓ0-minimisation problem and non-negative fraction function minimisation problem, and the equivalence between non-negative fraction function minimisation problem and regularised non-negative fraction function minimisation problem. It is proved that the optimal solution to the non-negative $\ell _0$ℓ0-minimisation problem could be approximately obtained by solving their regularised non-negative fraction function minimisation problem if some specific conditions are satisfied. Then, they propose a non-negative iterative thresholding algorithm to solve their regularised non-negative fraction function minimisation problem. At last, numerical experiments on some sparse non-negative signal recovery problems are reported. Many practical problems in the real world can be formulated as the non‐negative ℓ0 ‐minimisation problems, which seek the sparsest non‐negative signals to underdetermined linear equations. They have been widely applied in signal and image processing, machine learning, pattern recognition and computer vision. Unfortunately, this non‐negative ℓ0 ‐minimisation problem is non‐deterministic polynomial hard (NP‐hard) because of the discrete and discontinuous nature of the ℓ0 ‐norm. Inspired by the good performances of the fraction function in the authors’ former work, in this paper, the authors replace the ℓ0 ‐norm with the non‐convex fraction function and study the minimisation problem of the fraction function in recovering the sparse non‐negative signal from an underdetermined linear equation. They discuss the equivalence between non‐negative ℓ0 ‐minimisation problem and non‐negative fraction function minimisation problem, and the equivalence between non‐negative fraction function minimisation problem and regularised non‐negative fraction function minimisation problem. It is proved that the optimal solution to the non‐negative ℓ0 ‐minimisation problem could be approximately obtained by solving their regularised non‐negative fraction function minimisation problem if some specific conditions are satisfied. Then, they propose a non‐negative iterative thresholding algorithm to solve their regularised non‐negative fraction function minimisation problem. At last, numerical experiments on some sparse non‐negative signal recovery problems are reported. |
| Author | Cui, Angang Li, Haiyang Peng, Jigen Wen, Meng |
| Author_xml | – sequence: 1 givenname: Angang surname: Cui fullname: Cui, Angang organization: 1School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China – sequence: 2 givenname: Jigen surname: Peng fullname: Peng, Jigen email: jgpengxjtu@126.com organization: 2School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, People's Republic of China – sequence: 3 givenname: Haiyang surname: Li fullname: Li, Haiyang organization: 2School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, People's Republic of China – sequence: 4 givenname: Meng surname: Wen fullname: Wen, Meng organization: 3School of Science, Xi'an Polytechnic University, Xi'an 710048, People's Republic of China |
| BookMark | eNqFkLFOwzAQhi1UJNrCA7DlBVxsx45tNqgoVKrEUJgtJ7ErV8GJ7ATUt8dREANDme5O-r-707cAM996A8AtRiuMqLxzpoexCyuCsFgxxIoLMMecYSgLkc9-e86uwCLGI0oJhskcbPedDtFkaR305qB792my6A5eN1kwVetjH4aqd63Phuj8IbNBT6Md_NR0JoX70zW4tLqJ5uanLsH75ult_QJ3r8_b9cMOVjnnEuqyFmVRYVHbkiFCCoo1sxzVFglKNBFaUk14bk3N8rw0tJaCGlpgWRhGLM-XAE97q9DGGIxVXXAfOpwURmp0oZILlVyo0YUaXSSG_2Eq1-vx-z5o15wl7yfyyzXm9P8ptd_uyOMGIY5kguEEj7FjO4QkKp459g1nWYsj |
| CitedBy_id | crossref_primary_10_1049_ell2_13243 |
| Cites_doi | 10.1109/34.120331 10.1007/s00454-009-9221-z 10.1073/pnas.0502269102 10.1109/CVPR.2011.5995487 10.1016/j.cam.2017.12.048 10.1023/A:1018361916442 10.1287/ijoc.11.3.217 10.1137/060657704 10.1109/TSP.2010.2089624 10.1109/TSP.2010.2082536 10.1137/S0036139997327794 10.1080/02331939908844431 10.1007/s40305-014-0043-1 10.1007/978-1-4612-1009-2 10.1007/978-3-642-99789-1_13 10.1007/978-1-4419-7011-4 10.1007/978-0-8176-4948-7 10.1007/978-3-642-01513-7_49 10.1137/040615043 10.1109/TSP.2013.2281030 10.1214/aos/1176344136 10.1109/TIT.2008.929920 10.1109/ICIP.2010.5651881 |
| ContentType | Journal Article |
| Copyright | The Institution of Engineering and Technology 2021 The Institution of Engineering and Technology |
| Copyright_xml | – notice: The Institution of Engineering and Technology – notice: 2021 The Institution of Engineering and Technology |
| DBID | AAYXX CITATION |
| DOI | 10.1049/iet-spr.2018.5056 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1751-9683 |
| EndPage | 132 |
| ExternalDocumentID | 10_1049_iet_spr_2018_5056 SIL2BF00709 |
| Genre | article |
| GrantInformation_xml | – fundername: Science Foundations of Shaanxi Province of China grantid: 2015JM1012 – fundername: Science Foundations of Shaanxi Province of China grantid: 2016JQ1029 – fundername: National Natural Science Foundations of China grantid: 91730306 – fundername: National Natural Science Foundations of China grantid: 11771347 – fundername: National Natural Science Foundations of China grantid: 41390454 – fundername: National Natural Science Foundations of China grantid: 11271297 – fundername: Science Foundations of Shaanxi Province of China funderid: 2015JM1012 – fundername: National Natural Science Foundations of China funderid: 11771347 – fundername: National Natural Science Foundations of China funderid: 41390454 – fundername: National Natural Science Foundations of China funderid: 91730306 – fundername: Science Foundations of Shaanxi Province of China funderid: 2016JQ1029 – fundername: National Natural Science Foundations of China funderid: 11271297 |
| GroupedDBID | 0R 24P 29I 4.4 5GY 6IK 8FE 8FG AAJGR ABJCF ACGFS ACIWK AENEX AFKRA ALMA_UNASSIGNED_HOLDINGS ARAPS BENPR BFFAM BGLVJ CS3 DU5 EBS EJD HCIFZ HZ IFIPE IPLJI J9A JAVBF K6V K7- L6V LAI LOTEE LXI LXU M43 M7S NADUK NXXTH O9- OCL P2P P62 PTHSS RIE RNS RUI S0W UNMZH UNR ZZ .DC 0R~ 0ZK 1OC AAHHS AAHJG ABMDY ABQXS ACCFJ ACCMX ACESK ACGFO ACXQS ADEYR ADZOD AEEZP AEGXH AEQDE AIAGR AIWBW AJBDE ALUQN AVUZU CCPQU GROUPED_DOAJ HZ~ IAO IGS ITC MCNEO OK1 ~ZZ AAMMB AAYXX AEFGJ AFFHD AGXDD AIDQK AIDYY CITATION IDLOA PHGZM PHGZT PQGLB WIN |
| ID | FETCH-LOGICAL-c3779-abd8b6c18dfb5022641a5f70df0842a28a94a273fed533be4d984e46196e52f73 |
| IEDL.DBID | 24P |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000467406600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1751-9675 1751-9683 |
| IngestDate | Wed Oct 29 21:26:58 EDT 2025 Tue Nov 18 22:38:59 EST 2025 Wed Jan 22 16:31:07 EST 2025 Tue Jan 05 21:44:08 EST 2021 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | image processing iterative methods NP-hard nonnegative iterative thresholding signal processing nonnegative signal recovery problems machine learning fraction function penalty nonnegative fraction function minimisation problem computer vision underdetermined linear equations signal reconstruction sparse nonnegative signal reconstruction minimisation learning (artificial intelligence) nonconvex fraction function computational complexity pattern recognition |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3779-abd8b6c18dfb5022641a5f70df0842a28a94a273fed533be4d984e46196e52f73 |
| OpenAccessLink | https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-spr.2018.5056 |
| PageCount | 8 |
| ParticipantIDs | crossref_primary_10_1049_iet_spr_2018_5056 crossref_citationtrail_10_1049_iet_spr_2018_5056 wiley_primary_10_1049_iet_spr_2018_5056_SIL2BF00709 iet_journals_10_1049_iet_spr_2018_5056 |
| ProviderPackageCode | RUI |
| PublicationCentury | 2000 |
| PublicationDate | April 2019 |
| PublicationDateYYYYMMDD | 2019-04-01 |
| PublicationDate_xml | – month: 04 year: 2019 text: April 2019 |
| PublicationDecade | 2010 |
| PublicationTitle | IET signal processing |
| PublicationYear | 2019 |
| Publisher | The Institution of Engineering and Technology |
| Publisher_xml | – name: The Institution of Engineering and Technology |
| References | Bardsley, J.M.; Nagy, J.G. (C2) 2006; 27 Khajehnejad, M.A.; Dimakis, A.G.; Xu, W.Y. (C5) 2011; 59 Bradley, P.S.; Mangasarian, O.L.; Rosen, J.B. (C9) 1998; 11 Donoho, D.L.; Tanner, J. (C4) 2010; 43 Zhao, Y.B. (C18) 2014; 2 Donoho, D.L.; Tanner, J. (C19) 2005; 102 Bruckstein, A.M.; Elad, M.; Zibulevsky, M. (C3) 2008; 54 Bruckstein, A.M.; Donoho, D.L.; Elad, M. (C15) 2009; 51 Zhao, Y.B. (C20) 2013; 61 Donoho, D.L.; Tanner, J. (C1) 2005; 102 Wang, M.; Xu, W.Y.; Tang, A. (C8) 2011; 59 Mangasarian, O.L. (C13) 1999; 45 Nikolova, M. (C25) 2000; 61 Geman, D.; Reynolds, G. (C24) 1992; 14 Bradley, P.S.; Fayyad, U.M.; Mangasarian, O.L. (C10) 1999; 11 Cui, A.G.; Peng, J.G.; Li, H.Y. (C23) 2018; 336 Schwarz, G. (C26) 1978; 6 2010; 43 2009; 51 2014; 2 2018; 336 2010 2005; 102 2006; 27 2013; 61 2011; 42 2000; 61 1999; 45 1999; 11 1996 2008; 54 1992; 14 2011; 59 2013 2009; 5553 1998; 11 1989 1978; 6 e_1_2_8_27_2 e_1_2_8_28_2 e_1_2_8_23_2 e_1_2_8_24_2 e_1_2_8_25_2 e_1_2_8_26_2 e_1_2_8_9_2 e_1_2_8_2_2 e_1_2_8_4_2 e_1_2_8_3_2 e_1_2_8_6_2 e_1_2_8_5_2 e_1_2_8_8_2 e_1_2_8_7_2 e_1_2_8_20_2 e_1_2_8_21_2 e_1_2_8_22_2 e_1_2_8_16_2 e_1_2_8_17_2 e_1_2_8_18_2 e_1_2_8_19_2 e_1_2_8_12_2 e_1_2_8_13_2 e_1_2_8_14_2 e_1_2_8_15_2 e_1_2_8_10_2 e_1_2_8_11_2 |
| References_xml | – volume: 59 start-page: 1007 issue: 3 year: 2011 end-page: 1016 ident: C8 article-title: A unique non-negative solution to an underdetermined system: from vectors to matrices publication-title: IEEE Trans. Signal Process. – volume: 11 start-page: 5 year: 1998 end-page: 21 ident: C9 article-title: Parsimonious least norm approximation publication-title: Comput. Optim. Appl. – volume: 11 start-page: 217 year: 1999 end-page: 238 ident: C10 article-title: Mathematical programming for data mining: formulations and challenges publication-title: INFORMS J. Comput. – volume: 59 start-page: 196 issue: 1 year: 2011 end-page: 208 ident: C5 article-title: Sparse recovery of non-negative signals with minima expansion publication-title: IEEE Trans. Signal Process. – volume: 45 start-page: 149 issue: 1–4 year: 1999 end-page: 162 ident: C13 article-title: Minimum-support solutions of polyhedral concave programs publication-title: Optimization – volume: 2 start-page: 171 issue: 2 year: 2014 end-page: 193 ident: C18 article-title: Equivalence and strong equivalence between the sparsest and least -norm non-negative solutions of linear systems and their applications publication-title: J. Oper. Res. Soc. China – volume: 102 start-page: 9446 issue: 27 year: 2005 end-page: 9451 ident: C1 article-title: Sparse nonnegative solution of underdetermined linear equations by linear programming publication-title: Proc. Natl. Acad. Sci. USA – volume: 27 start-page: 1184 issue: 4 year: 2006 end-page: 1197 ident: C2 article-title: Covariance-preconditioned iterative methods for non-negativity constrained astronomical imaging publication-title: SIAM J. Matrix Anal. Appl. – volume: 61 start-page: 633 issue: 2 year: 2000 end-page: 658 ident: C25 article-title: Local strong homogeneity of a regularized estimator publication-title: SIAM J. Appl. Math. – volume: 6 start-page: 461 issue: 2 year: 1978 end-page: 464 ident: C26 article-title: Estimating the dimension of a model publication-title: Ann. Stat. – volume: 43 start-page: 522 issue: 3 year: 2010 end-page: 541 ident: C4 article-title: Counting the faces of randomly projected hypercubes and orthants with applications publication-title: Discrete Comput. Geom. – volume: 102 start-page: 9446 issue: 27 year: 2005 end-page: 9451 ident: C19 article-title: Sparse non-negative solutions of underdetermined linear equations by linear programming publication-title: Proc. Natl. Acad. Sci. USA – volume: 61 start-page: 5777 issue: 22 year: 2013 end-page: 5788 ident: C20 article-title: RSP-based analysis for sparsest and least -norm solutions to underdetermined linear systems publication-title: IEEE Trans. Signal Process. – volume: 54 start-page: 4813 issue: 11 year: 2008 end-page: 4820 ident: C3 article-title: On the uniqueness of non-negative sparse solutions to underdetermined systems of equations publication-title: IEEE Trans. Inf. Theory – volume: 336 start-page: 353 year: 2018 end-page: 374 ident: C23 article-title: Affine matrix rank minimization problem via non-convex fraction function penalty publication-title: J. Comput. Appl. Math. – volume: 51 start-page: 34 issue: 1 year: 2009 end-page: 81 ident: C15 article-title: From sparse solutions of systems of equations to sparse modelling of signals and images publication-title: SIAM Rev. – volume: 14 start-page: 367 issue: 3 year: 1992 end-page: 383 ident: C24 article-title: Constrained restoration and the recovery of discontinuities publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 43 start-page: 522 issue: 3 year: 2010 end-page: 541 article-title: Counting the faces of randomly projected hypercubes and orthants with applications publication-title: Discrete Comput. Geom. – start-page: 1917 year: 2010 end-page: 1920 article-title: A split Bregman method for non‐negative sparsity penalized least squares with applications to hyperspectral demixing – volume: 102 start-page: 9446 issue: 27 year: 2005 end-page: 9451 article-title: Sparse nonnegative solution of underdetermined linear equations by linear programming publication-title: Proc. Natl. Acad. Sci. USA – volume: 59 start-page: 196 issue: 1 year: 2011 end-page: 208 article-title: Sparse recovery of non‐negative signals with minima expansion publication-title: IEEE Trans. Signal Process. – volume: 2 start-page: 171 issue: 2 year: 2014 end-page: 193 article-title: Equivalence and strong equivalence between the sparsest and least ‐norm non‐negative solutions of linear systems and their applications publication-title: J. Oper. Res. Soc. China – volume: 59 start-page: 1007 issue: 3 year: 2011 end-page: 1016 article-title: A unique non‐negative solution to an underdetermined system: from vectors to matrices publication-title: IEEE Trans. Signal Process. – volume: 11 start-page: 217 year: 1999 end-page: 238 article-title: Mathematical programming for data mining: formulations and challenges publication-title: INFORMS J. Comput. – volume: 14 start-page: 367 issue: 3 year: 1992 end-page: 383 article-title: Constrained restoration and the recovery of discontinuities publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 54 start-page: 4813 issue: 11 year: 2008 end-page: 4820 article-title: On the uniqueness of non‐negative sparse solutions to underdetermined systems of equations publication-title: IEEE Trans. Inf. Theory – volume: 42 start-page: 2849 issue: 7 year: 2011 end-page: 2856 article-title: Non‐negative sparse coding for discriminative semi‐supervised learning – volume: 45 start-page: 149 issue: 1–4 year: 1999 end-page: 162 article-title: Minimum‐support solutions of polyhedral concave programs publication-title: Optimization – year: 1989 – volume: 61 start-page: 633 issue: 2 year: 2000 end-page: 658 article-title: Local strong homogeneity of a regularized estimator publication-title: SIAM J. Appl. Math. – volume: 61 start-page: 5777 issue: 22 year: 2013 end-page: 5788 article-title: RSP‐based analysis for sparsest and least ‐norm solutions to underdetermined linear systems publication-title: IEEE Trans. Signal Process. – start-page: 175 year: 1996 end-page: 188 – volume: 27 start-page: 1184 issue: 4 year: 2006 end-page: 1197 article-title: Covariance‐preconditioned iterative methods for non‐negativity constrained astronomical imaging publication-title: SIAM J. Matrix Anal. Appl. – volume: 51 start-page: 34 issue: 1 year: 2009 end-page: 81 article-title: From sparse solutions of systems of equations to sparse modelling of signals and images publication-title: SIAM Rev. – volume: 11 start-page: 5 year: 1998 end-page: 21 article-title: Parsimonious least norm approximation publication-title: Comput. Optim. Appl. – volume: 6 start-page: 461 issue: 2 year: 1978 end-page: 464 article-title: Estimating the dimension of a model publication-title: Ann. Stat. – volume: 5553 start-page: 449 year: 2009 end-page: 456 article-title: Non‐negative‐least‐square classifier for face recognition – volume: 102 start-page: 9446 issue: 27 year: 2005 end-page: 9451 article-title: Sparse non‐negative solutions of underdetermined linear equations by linear programming publication-title: Proc. Natl. Acad. Sci. USA – year: 2010 – volume: 336 start-page: 353 year: 2018 end-page: 374 article-title: Affine matrix rank minimization problem via non‐convex fraction function penalty publication-title: J. Comput. Appl. Math. – year: 2013 – ident: e_1_2_8_25_2 doi: 10.1109/34.120331 – ident: e_1_2_8_5_2 doi: 10.1007/s00454-009-9221-z – ident: e_1_2_8_2_2 doi: 10.1073/pnas.0502269102 – ident: e_1_2_8_12_2 doi: 10.1109/CVPR.2011.5995487 – ident: e_1_2_8_24_2 doi: 10.1016/j.cam.2017.12.048 – ident: e_1_2_8_10_2 doi: 10.1023/A:1018361916442 – ident: e_1_2_8_11_2 doi: 10.1287/ijoc.11.3.217 – ident: e_1_2_8_16_2 doi: 10.1137/060657704 – ident: e_1_2_8_9_2 doi: 10.1109/TSP.2010.2089624 – ident: e_1_2_8_6_2 doi: 10.1109/TSP.2010.2082536 – ident: e_1_2_8_26_2 doi: 10.1137/S0036139997327794 – ident: e_1_2_8_23_2 – ident: e_1_2_8_14_2 doi: 10.1080/02331939908844431 – ident: e_1_2_8_18_2 – ident: e_1_2_8_19_2 doi: 10.1007/s40305-014-0043-1 – ident: e_1_2_8_20_2 doi: 10.1073/pnas.0502269102 – ident: e_1_2_8_22_2 doi: 10.1007/978-1-4612-1009-2 – ident: e_1_2_8_13_2 doi: 10.1007/978-3-642-99789-1_13 – ident: e_1_2_8_17_2 doi: 10.1007/978-1-4419-7011-4 – ident: e_1_2_8_28_2 doi: 10.1007/978-0-8176-4948-7 – ident: e_1_2_8_8_2 doi: 10.1007/978-3-642-01513-7_49 – ident: e_1_2_8_7_2 – ident: e_1_2_8_3_2 doi: 10.1137/040615043 – ident: e_1_2_8_21_2 doi: 10.1109/TSP.2013.2281030 – ident: e_1_2_8_27_2 doi: 10.1214/aos/1176344136 – ident: e_1_2_8_4_2 doi: 10.1109/TIT.2008.929920 – ident: e_1_2_8_15_2 doi: 10.1109/ICIP.2010.5651881 |
| SSID | ssj0056512 |
| Score | 2.1384268 |
| Snippet | Many practical problems in the real world can be formulated as the non-negative $\ell _0$ℓ0-minimisation problems, which seek the sparsest non-negative signals... Many practical problems in the real world can be formulated as the non‐negative ℓ0 ‐minimisation problems, which seek the sparsest non‐negative signals to... Many practical problems in the real world can be formulated as the non‐negative ‐minimisation problems, which seek the sparsest non‐negative signals to... |
| SourceID | crossref wiley iet |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 125 |
| SubjectTerms | computational complexity computer vision fraction function penalty image processing iterative methods learning (artificial intelligence) machine learning minimisation nonconvex fraction function nonnegative fraction function minimisation problem nonnegative iterative thresholding nonnegative signal recovery problems NP‐hard pattern recognition Research Article signal processing signal reconstruction sparse nonnegative signal reconstruction underdetermined linear equations |
| Title | Sparse non-negative signal reconstruction using fraction function penalty |
| URI | http://digital-library.theiet.org/content/journals/10.1049/iet-spr.2018.5056 https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fiet-spr.2018.5056 |
| Volume | 13 |
| WOSCitedRecordID | wos000467406600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Open Access: Wiley-Blackwell Open Access Journals customDbUrl: eissn: 1751-9683 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0056512 issn: 1751-9675 databaseCode: 24P dateStart: 20130101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 1751-9683 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0056512 issn: 1751-9675 databaseCode: WIN dateStart: 20130101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dS8MwEA9z-qAPfovziz6ID0K1H2mTPOpwOJQxmOLeStokYyCzbEPwzT_Bv9G_xLumKw5hgvhSSnoJ4S6Xu2tyvyPkVMZZpKIYFImp2KWYoytTFrlhBluhMSIw1BabYJ0O7_dFt0aas1wYiw9R_XBDzSj2a1RwmdoqJODUghCHeupOcoT09PkF2vElsuz7IcOlHdDubDsGh8UeeTKsJx_zsDraFJc_hpgzTkvwed5lLWxOa-NfZrtJ1kuX07mya2SL1PRom6x9AyLcIXe9HAJc7YxeRp_vHyM9KODAHbzcAT2LoLkCmnXwqvzAMWObEuGgZSxecg3E07dd8ti6eWjeumWZBTdDtEGQjeJpnPlcmTTyMLHWl5FhnjIep4EMuBRUgpdjtALfMNVUCU41hcgr1lFgWLhH6jA9vU8crShF6fqYcutRxalkSEipgcBI0wbxZvxNshKDHEthPCfFWTgVCfApAT4lyKcE-dQg51WX3AJwLCI-w7ZSDSeLCMNCWr8PmfTa98F1C2GRxMGfeh2SVWgX9rrPEamDuPQxWclep8PJ-KRYrPB8ane-ABZt7j0 |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8NAEB7aKqgH32J95iAehGiaTpLdo6_S0loKrdBbSLO7pSCxtEXw5k_wN_pLnEnaYhEUxFtIZpcws7MzszvzDcBZ5Mee8nxSpED5NnKNbtQLPLsc01ZojHQNZs0mgmZTdLuylYO7WS1Mhg8xP3BjzUj3a1ZwPpDOAk5kkMyBntjjIWN6lsQlG_I8LCFZJ07sc7E124_JY8nuPANuKO-L8vxuU159m2LBOuXp86LPmhqdysb__O4mrE-dTus6WyVbkNPJNqx9gSLcgXp7SCGutpLn5OPtPdH9FBDc4vQOGpmGzXOoWYuT5fuWGWVFERbbxvRhqIl48roLj5X7zm3VnjZasGPGGyTpKNHz45JQpuc5XFpbijwTOMo4At3IFZHEiPwcoxV5hz2NSgrUxGvpa881QXkPCvR7eh8srRBZviUuunVQCYwCJkQ0FBppLIIzY3AYT1HIuRnGU5jehqMMiU8h8SlkPoXMpyJczIcMMwiOn4jP-d1UEcc_EZZTcf0-ZdiuNdybCgMjyYM_jTqFlWrnoRE2as36IawSjcySf46gQKLTx7Acv0wG49FJunI_AZ678RI |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1fS8NADA_bFNEH_4vzbx_EB6HadWl7fVTncGyMwRT2Vrre3RhILdsQfPMj-Bn9JCbXbTgEBfGttLnjSC6XpLn8AnAW-4knPZ8UKZC-jVyjG_cDz64mdBRqHboa82YTQbster2wU4DarBYmx4eY_3BjzTDnNSu4yqTOA05kkMyhmtjjjDE9K-KSDXkRltALjHq62Jmdx-Sx5DnPgBvK-6I6z22GV9-mWLBORfq86LMao1Pf-J_lbsL61Om0rvNdsgUFlW7D2hcowh1odjMKcZWVPqcfb--pGhhAcIuvd9BIEzbPoWYtviw_sPQoL4qw2Daah0wR8eR1Fx7rdw-39_a00YKdMN4gSUeKvp9UhNR9z-HS2krs6cCR2hHoxq6IQ4zJz9FKknfYVyhDgQop9vKV5-qgugclWp7aB0tJRJZvhYtuHZQC44AJETWFRgrL4MwYHCVTFHJuhvEUmWw4hhHxKSI-RcyniPlUhov5kCyH4PiJ-JzfTRVx_BNh1Yjr9ymjbqPl3tQZGCk8-NOoU1jp1OpRq9FuHsIqkYT53Z8jKJHk1DEsJy-T4Xh0YjbuJz9i8I0 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Sparse+non-negative+signal+reconstruction+using+fraction+function+penalty&rft.jtitle=IET+signal+processing&rft.au=Cui%2C+Angang&rft.au=Peng%2C+Jigen&rft.au=Li%2C+Haiyang&rft.au=Wen%2C+Meng&rft.date=2019-04-01&rft.pub=The+Institution+of+Engineering+and+Technology&rft.issn=1751-9675&rft.eissn=1751-9683&rft.volume=13&rft.issue=2&rft.spage=125&rft.epage=132&rft_id=info:doi/10.1049%2Fiet-spr.2018.5056&rft.externalDocID=10_1049_iet_spr_2018_5056 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1751-9675&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1751-9675&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1751-9675&client=summon |