Precise Undersampling Theorems
Undersampling theorems state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest-provided the object in question obeys a sparsity condition, the samples measure appropriate linear combinations of signal values, and we reconstruct w...
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Published in | Proceedings of the IEEE Vol. 98; no. 6; pp. 913 - 924 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.06.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
ISSN | 0018-9219 1558-2256 |
DOI | 10.1109/JPROC.2010.2045630 |
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Abstract | Undersampling theorems state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest-provided the object in question obeys a sparsity condition, the samples measure appropriate linear combinations of signal values, and we reconstruct with a particular nonlinear procedure. While there are many ways to crudely demonstrate such undersampling phenomena, we know of only one mathematically rigorous approach which precisely quantifies the true sparsity-undersampling tradeoff curve of standard algorithms and standard compressed sensing matrices. That approach, based on combinatorial geometry, predicts the exact location in sparsity-undersampling domain where standard algorithms exhibit phase transitions in performance. We review the phase transition approach here and describe the broad range of cases where it applies. We also mention exceptions and state challenge problems for future research. Sample result: one can efficiently reconstruct a k-sparse signal of length N from n measurements, provided n ¿ 2k · log(N/n), for (k,n,N) large.k ¿ N. AMS 2000 subject classifications . Primary: 41A46, 52A22, 52B05, 62E20, 68P30, 94A20; Secondary: 15A52, 60F10, 68P25, 90C25, 94B20. |
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AbstractList | Undersampling theorems state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest--provided the object in question obeys a sparsity condition, the samples measure appropriate linear combinations of signal values, and we reconstruct with a particular nonlinear procedure. While there are many ways to crudely demonstrate such undersampling phenomena, we know of only one mathematically rigorous approach which precisely quantifies the true sparsity-undersampling tradeoff curve of standard algorithms and standard compressed sensing matrices. That approach, based on combinatorial geometry, predicts the exact location in sparsity-undersampling domain where standard algorithms exhibit phase transitions in performance. We review the phase transition approach here and describe the broad range of cases where it applies. We also mention exceptions and state challenge problems for future research. Sample result: one can efficiently reconstruct a k -sparse signal of length N from N measurements, provided n Unknown character [gap] Unknown character 2 k Unknown character times Unknown character log ( N / n ) , for ( k , n , N ) large, k [Lt] N . AMS 2000 subject classifications . Primary: 41A46, 52A22, 52B05, 62E20, 68P30, 94A20; Secondary: 15A52, 60F10, 68P25, 90C25, 94B20. Undersampling theorems state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest-provided the object in question obeys a sparsity condition, the samples measure appropriate linear combinations of signal values, and we reconstruct with a particular nonlinear procedure. While there are many ways to crudely demonstrate such undersampling phenomena, we know of only one mathematically rigorous approach which precisely quantifies the true sparsity-undersampling tradeoff curve of standard algorithms and standard compressed sensing matrices. That approach, based on combinatorial geometry, predicts the exact location in sparsity-undersampling domain where standard algorithms exhibit phase transitions in performance. We review the phase transition approach here and describe the broad range of cases where it applies. We also mention exceptions and state challenge problems for future research. Sample result: one can efficiently reconstruct a k-sparse signal of length N from n measurements, provided n ?? 2k ?? log(N/n), for (k,n,N) large.k ?? N. AMS 2000 subject classifications . Primary: 41A46, 52A22, 52B05, 62E20, 68P30, 94A20; Secondary: 15A52, 60F10, 68P25, 90C25, 94B20. Undersampling theorems state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest--provided the object in question obeys a sparsity condition, the samples measure appropriate linear combinations of signal values, and we reconstruct with a particular nonlinear procedure. While there are many ways to crudely demonstrate such undersampling phenomena, we know of only one mathematically rigorous approach which precisely quantifies the true sparsity-undersampling tradeoff curve of standard algorithms and standard compressed sensing matrices. That approach, based on combinatorial geometry, predicts the exact location in sparsity-undersampling domain where standard algorithms exhibit phase transitions in performance. We review the phase transition approach here and describe the broad range of cases where it applies. We also mention exceptions and state challenge problems for future research. Sample result: one can efficiently reconstruct a [Formula Omitted]-sparse signal of length [Formula Omitted] from [Formula Omitted] measurements, provided [Formula Omitted], for [Formula Omitted] large, [Formula Omitted]. AMS 2000 subject classifications . Primary: 41A46, 52A22, 52B05, 62E20, 68P30, 94A20; Secondary: 15A52, 60F10, 68P25, 90C25, 94B20. Undersampling theorems state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest-provided the object in question obeys a sparsity condition, the samples measure appropriate linear combinations of signal values, and we reconstruct with a particular nonlinear procedure. While there are many ways to crudely demonstrate such undersampling phenomena, we know of only one mathematically rigorous approach which precisely quantifies the true sparsity-undersampling tradeoff curve of standard algorithms and standard compressed sensing matrices. That approach, based on combinatorial geometry, predicts the exact location in sparsity-undersampling domain where standard algorithms exhibit phase transitions in performance. We review the phase transition approach here and describe the broad range of cases where it applies. We also mention exceptions and state challenge problems for future research. Sample result: one can efficiently reconstruct a k-sparse signal of length N from n measurements, provided n ¿ 2k · log(N/n), for (k,n,N) large.k ¿ N. AMS 2000 subject classifications . Primary: 41A46, 52A22, 52B05, 62E20, 68P30, 94A20; Secondary: 15A52, 60F10, 68P25, 90C25, 94B20. |
Author | Tanner, Jared Donoho, David L. |
Author_xml | – sequence: 1 givenname: David L. surname: Donoho fullname: Donoho, David L. email: donoho@stanford.edu organization: Department of Statistics, Stanford University, Stanford , CA, USA – sequence: 2 givenname: Jared surname: Tanner fullname: Tanner, Jared email: jared.tanner@ed.ac.uk organization: School of Mathematics, University of Edinburgh, Edinburgh, U.K |
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Cites_doi | 10.1137/090748160 10.1098/rsta.2009.0152 10.1002/cpa.20131 10.1002/cpa.20124 10.1007/s00454-009-9221-z 10.1007/s00454-005-1220-0 10.1016/j.acha.2009.04.002 10.1002/cpa.20227 10.1109/TIT.2010.2040892 10.1073/pnas.0437847100 10.1109/TIT.2003.820031 10.1016/j.acha.2008.07.002 10.1007/s00454-007-9034-x 10.1109/18.959265 10.1073/pnas.0502269102 10.1109/JSTSP.2009.2039176 10.1111/j.2517-6161.1992.tb01864.x 10.1073/pnas.0502258102 10.1109/TIT.2009.2016018 10.1109/TIT.2005.858979 10.1073/pnas.0909892106 10.1109/TIT.2002.801410 10.1109/MSP.2007.914728 10.1109/TIT.2008.929958 10.1109/ALLERTON.2008.4797608 10.1002/mrm.21391 10.1016/j.acha.2010.07.001 10.1016/j.acha.2008.09.001 10.1109/TIT.2009.2016006 10.1090/S0894-0347-08-00600-0 10.1109/TIT.2005.862083 10.1109/ALLERTON.2008.4797639 10.1007/BF02187839 10.1109/TIT.2004.834793 |
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References | ref35 ref13 ref34 ref37 ref36 ref14 adamczak (ref18) 2009 ref31 ref30 ref11 ref32 ref10 donoho (ref33) 2008 ref2 ref17 ref38 ref16 ref19 jansson (ref9) 1997 fuchs (ref8) 2005; 5 (ref1) 0 stojnic (ref21) 2009 ref24 foucart (ref43) 2009; 26 donoho (ref12) 2004 ref45 ref23 ref26 ref25 ref20 ref42 ref22 donoho (ref6) 1992; 54 obozinski (ref41) 0 jafarpour (ref39) 2008 ref28 vershik (ref15) 1992; 11 ref27 ref29 ref7 ref4 ref3 ref5 ref40 (ref44) 0 |
References_xml | – ident: ref32 doi: 10.1137/090748160 – year: 0 ident: ref1 publication-title: Seeking sparse solutions to linear systems of equations – ident: ref17 doi: 10.1098/rsta.2009.0152 – ident: ref29 doi: 10.1002/cpa.20131 – ident: ref4 doi: 10.1002/cpa.20124 – volume: 5 start-page: v/729 year: 2005 ident: ref8 article-title: sparsity and uniqueness for some specific underdetermined linear systems publication-title: Proc IEEE Int Conf Acoust Speech Signal Process – ident: ref10 doi: 10.1007/s00454-009-9221-z – year: 0 ident: ref44 – year: 1997 ident: ref9 publication-title: Deconvolution of Images and Spectra – ident: ref11 doi: 10.1007/s00454-005-1220-0 – ident: ref38 doi: 10.1016/j.acha.2009.04.002 – ident: ref31 doi: 10.1002/cpa.20227 – ident: ref16 doi: 10.1109/TIT.2010.2040892 – year: 0 ident: ref41 article-title: support union recovery in high-dimensional multivariate regression publication-title: Ann Stat – ident: ref26 doi: 10.1073/pnas.0437847100 – ident: ref25 doi: 10.1109/TIT.2003.820031 – ident: ref36 doi: 10.1016/j.acha.2008.07.002 – ident: ref19 doi: 10.1007/s00454-007-9034-x – ident: ref23 doi: 10.1109/18.959265 – year: 2008 ident: ref39 publication-title: Efficient and robust compressed sensing using high-quality expander graphs – year: 2008 ident: ref33 publication-title: Sparse Solution of Underdetermined Linear Equations by Stagewise Orthogonal Matching Pursuit – ident: ref7 doi: 10.1073/pnas.0502269102 – ident: ref35 doi: 10.1109/JSTSP.2009.2039176 – volume: 54 start-page: 41 year: 1992 ident: ref6 article-title: maximum entropy and the nearly black object publication-title: J R Stat Soc B doi: 10.1111/j.2517-6161.1992.tb01864.x – ident: ref13 doi: 10.1073/pnas.0502258102 – ident: ref22 doi: 10.1109/TIT.2009.2016018 – ident: ref30 doi: 10.1109/TIT.2005.858979 – year: 2004 ident: ref12 publication-title: Neighborly polytopes and sparse solution of underdetermined linear equations – year: 2009 ident: ref18 publication-title: Compressed sensing matrices with independent columns and neighborly polytopes by random sampling – ident: ref45 doi: 10.1073/pnas.0909892106 – ident: ref24 doi: 10.1109/TIT.2002.801410 – ident: ref3 doi: 10.1109/MSP.2007.914728 – volume: 11 start-page: 181 year: 1992 ident: ref15 article-title: asymptotic behavior of the number of faces of random polyhedra and the neighborliness problem publication-title: Selecta Math Sov – ident: ref34 doi: 10.1109/TIT.2008.929958 – ident: ref20 doi: 10.1109/ALLERTON.2008.4797608 – ident: ref2 doi: 10.1002/mrm.21391 – ident: ref42 doi: 10.1016/j.acha.2010.07.001 – volume: 26 start-page: 395 year: 2009 ident: ref43 article-title: sparsest solutions of underdetermined linear systems via <tex notation="tex">$ \ell_{q}$</tex>-minimization for <tex notation="tex">$0\ <\ q\leq 1$</tex> publication-title: Appl Comput Harmon Anal doi: 10.1016/j.acha.2008.09.001 – ident: ref37 doi: 10.1109/TIT.2009.2016006 – ident: ref5 doi: 10.1090/S0894-0347-08-00600-0 – ident: ref28 doi: 10.1109/TIT.2005.862083 – year: 2009 ident: ref21 publication-title: Various thresholds for -optimization in compressed sensing – ident: ref40 doi: 10.1109/ALLERTON.2008.4797639 – ident: ref14 doi: 10.1007/BF02187839 – ident: ref27 doi: 10.1109/TIT.2004.834793 |
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Snippet | Undersampling theorems state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest-provided... Undersampling theorems state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest--provided... |
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SubjectTerms | Algorithms Bandlimited measurements Combinatorial analysis Compressed Compressed sensing ell_{1} -minimization Geometry Heart Image reconstruction Image sampling Length measurement Magnetic resonance imaging Mathematical analysis Particle measurements Phase transformations Phase transitions Psychology random measurements random polytopes Sampling Sampling methods Sparsity Studies superresolution Theorems undersampling universality of matrix ensembles |
Title | Precise Undersampling Theorems |
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