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Recent breakthrough results in compressive sensing (CS) have established that many high dimensional signals can be accurately recovered from a relatively small number of non-adaptive linear observations, provided that the signals possess a sparse representation in some basis.
“A dynamically adaptive imaging algorithm for wavelet-encoded MRI,”
L. P. Panych and F. A. Jolesz, · 1994
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“Wavelet-based statistical signal processing using hidden Markov models,”
M. S. Crouse, R. D. Nowak, and R. G. Baraniuk, · 1998
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“Bayesian tree-structured image modeling using wavelet-domain hidden Markov models,”
J. K. Romberg, H. Choi, and R. G. Baraniuk, · 2001
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“Fast reconstruction of piecewise smooth signals from incoherent projections,”
M. F. Duarte, M. B. Wakin, and R. G. Baraniuk, · 2005
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“Signal reconstruction using sparse tree representation,”
C. La and M. N. Do, · 2005
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“The Dantzig selector: Statistical estimation when p p is much larger than n n ,”
E. Candès and T. Tao, · 2007
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“Bayesian compressive sensing,”
S. Ji, Y. Xue, and L. Carin, · 2008
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“Finding needles in noisy haystacks,”
R. M. Castro, J. Haupt, R. Nowak, and G. M. Raz, · 2008
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“Optimal two-stage search for sparse targets using convex criteria,”
E. Bashan, R. Raich, and A. O. Hero, · 2008
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A wavelet tour of signal processing: The sparse way
S. Mallat, · 2008
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“Compressed sensing and Bayesian experimental design,”
M. W. Seeger and H. Nickisch, · 2008
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“Searching for a trail of evidence in a maze,”
E. Arias-Castro, E. J. Candès, H. Helgason, and O. Zeitouni, · 2008
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“Compressive distilled sensing: Sparse recovery using adaptivity in compressive measurements,”
J. Haupt, R. Baraniuk, R. Castro, and R. Nowak, · 2009
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“Learning with structured sparsity,”
J. Huang, T. Zhang, and D. Metaxas, · 2009
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“On the reconstruction of block-sparse signals with an optimal number of measurements,”
M. Stojnic, F. Parvaresh, and B. Hassibi, · 2009
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“Adaptive compressed image sensing based on wavelet modeling and direct sampling,”
S. Deutsch, A. Averbuch, and S. Dekel, · 2009
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“Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ 1 \ell_{1} -constrained quadratic programming (lasso),”
M. J. Wainwright, · 2009
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“Information-theoretic limitations on sparsity recovery in the high-dimensional and noisy setting,”
M. Wainwright, · 2009
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“Revisiting marginal regression,”
C. Genovese, J. Jin, and L. Wasserman, · 2009
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“Necessary and sufficient conditions for sparsity pattern recovery,”
A. K. Fletcher, S. Rangan, and V. K. Goyal, · 2009
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Introduction to nonparametric estimation
A. B. Tsybakov, · 2009
Cited alongside, same era.
“Near-ideal model selection by ℓ 1 \ell_{1} minimization,”
E. J. Candès Y. Plan, · 2009
Cited alongside, same era.
“Group lasso with overlap and graph lasso,”
L. Jacob, G. Obozinski, and J.-P. Vert, · 2009
Cited alongside, same era.
“The composite absolute penalties family for grouped and hierarchical variable selection,”
P. Zhao, G. Rocha, and B. Yu, · 2009
Cited alongside, same era.
“Adaptive sensing for sparse signal recovery,”
J. Haupt, R. Castro, and R. Nowak, · 2009
Cited alongside, same era.
“Adaptive search for sparse targets with informative priors,”
G. Newstadt, E. Bashan, and A. O. Hero, · 2010
Cited alongside, same era.
“Group lasso with overlaps: The latent group lasso approach,”
G. Obozinski, L. Jacob, and J.-P. Vert, · 2011
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Art of Computer Programming Volume 1: Fundamental Algorithms
D. E. Knuth, · 2011
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“Adaptive group testing strategies for target detection and localization in noisy environments,”
M. Iwen and A. Tewfik, · 2012
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“Sequential testing for sparse recovery,”
M. Malloy and R. Nowak, · 2012
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“Sequentially designed compressed sensing,”
J. Haupt, R. Baraniuk, R. Castro, and R. Nowak, · 2012
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“Model-based compressive sensing,”
R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, · 2010
Cited alongside, same era.
“Information theoretic bounds for compressed sensing,”
S. Aeron, V. Saligrama, and M. Zhao, · 2010
Cited alongside, same era.
“Information-theoretic limits on sparse signal recovery: Dense versus sparse measurement matrices,”
W. Wang, M. J. Wainwright, and K. Ramchandran, · 2010
Cited alongside, same era.
“Proximal methods for hierarchical sparse coding,”
R. Jenatton, J. Mairal, G. Obozinski, and F. Bach, · 2010
Cited alongside, same era.
“Distilled sensing: Adaptive sampling for sparse detection and estimation,”
J. Haupt, R. M. Castro, and R. Nowak, · 2011
Cited alongside, same era.
“Two-stage multiscale search for sparse targets,”
E. Bashan, G. Newstadt, and A. O. Hero, · 2011
Cited alongside, same era.
D. Wei and A. O. Hero, · 2012
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“Recovering block-structured activations using compressive measurements,”
S. Balakrishnan, M. Kolar, A. Rinaldo, and A. Singh, · 2012
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“Adaptive sensing performance lower bounds for sparse signal detection and support estimation,”
R. M. Castro, · 2012
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“Lower bounds for adaptive sparse recovery,”
E. Price and D. P. Woodruff, · 2012
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“Compressive imaging using approximate message passing and a Markov-tree prior,”
S. Som and P. Schniter, · 2012
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“Adaptive compressed image sensing using dictionaries,”
A. Averbuch, S. Dekel, and S. Deutsch, · 2012
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“The sampling rate-distortion tradeoff for sparsity pattern recovery in compressed sensing,”
G. Reeves and M. Gastpar, · 2012
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“Sharp support recovery from noisy random measurements by ℓ 1 \ell_{1} minimization,”
C. Dossal, M.-L. Chabanol, G. Peyré, and J. Fadili, · 2012
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“Compressive binary search,”
M. A. Davenport and E. Arias-Castro, · 2012
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“Near-optimal adaptive compressive sensing,”
M. Malloy and R. Nowak, · 2012
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“Recovering graph-structured activations using adaptive compressive measurements,”
A. Krishnamurthy, J. Sharpnack, and A. Singh, · 2013
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“Adaptive sensing with structured sparsity,”
N. Rao and R. Nowak, · 2013
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“Approximate sparsity pattern recovery: Information-theoretic lower bounds,”
G. Reeves and M. Gastpar, · 2013
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“How well can we estimate a sparse vector?,”
E. J. Candès and M. A. Davenport, · 2013
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