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 inProceedings of the IEEE Vol. 98; no. 6; pp. 913 - 924
Main Authors Donoho, David L., Tanner, Jared
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN0018-9219
1558-2256
DOI10.1109/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.
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.
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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
URI https://ieeexplore.ieee.org/document/5458001
https://www.proquest.com/docview/1027190404
https://www.proquest.com/docview/818832110
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