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Cited by 14Sparse and Compressive Sensing TechniquesStatistical Methods and InferenceMathematical Analysis and Transform Methods

The DOI matches its Crossref record; 151 of the 151 references it cites were found in a registry or an index.

Sparsity-Aware Learning and Compressed Sensing: An Overview

Theodoridis, Sergios, Kopsinis, Yannis, Slavakis, Konstantinos·2014·Academic Press Library in Signal Processing
DOI 10.1016/b978-0-12-396502-8.00023-1
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Cites

151 of 151 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 151 references

Research works printed but not linked: 17

  1. Maleki, 2011, Asymptotic analysis of complex LASSO via complex approximate message passing (CAMP), IEEE Trans. Inform. Theory
  2. Pinkus, 1989, vol. 93
  3. M. Wakin, Manifold-based signal recovery and parameter estimation from compressive measurements, 2008. preprint: http://arxiv.org/abs/1002.1247 · arXiv
  4. Y. Tsaig, Sparse solution of underdetermined linear systems: algorithms and applications, PhD Thesis, Stanford University, 2007.
  5. Hoch, 1990, Maximum entropy reconstruction of complex (phase sensitive) spectra, J. Magn. Reson., 86, 236
  6. Nesterov, 1983, A method for solving the convex programming problem with convergence rate O(1/k2), Dokl. Akad. Nauk SSSR, 269, 543
  7. S. Bakin, Adaptive regression and model selection in data mining problems, PhD Thesis, Australian National University, 1999.
  8. Cevher, 2010, Sparse signal recovery and acquisition with graphical models, IEEE Signal Process. Mag., 27, 92
  9. Langford, 2009, Sparse online learning via truncated gradient, J. Mach. Learn. Res., 10, 777
  10. Yamada, 2004, Hybrid steepest descent method for variational inequality problem over the fixed point set of certain quasi-nonexpansive mappings, Numer. Func. Anal. Optim., 25, 619
  11. Starck, 2007, The undecimated wavelet decomposition and its reconstruction, IEEE Trans. Signal Process., 16, 297
  12. S. Nam, M.E. Davies, M. Elad, R. Gribonval, The cosparse analysis model and algorithms, Appl. Comput. Harmon. Anal. (in press).
  13. Recht, 2011, A simpler approach to matrix completion, J. Mach. Learn. Res., 12, 3413
  14. Chen, 2011, Matrix completion via an alternating direction method, IMA J. Numer. Anal.
  15. Z. Lin, M. Chen, Y. Ma, The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices, September 2010. arXiv:1009.5055. · arXiv
  16. Toh, 2010, An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems, Pac. J. Optim., 6, 615
  17. Yuan, 2009, Sparse and low-rank matrix decomposition via alternating direction methods, Pac. J. Optim

Cited by

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Metadata

Authors
Theodoridis, S., Kopsinis, Y., & Slavakis, K.
Affiliations
  • National and Kapodistrian University of Athens (GR)
  • University of Minnesota (US)
Corresponding author
  • Theodoridis, Sergios
  • Kopsinis, Yannis
  • Slavakis, Konstantinos
Venue
Academic Press Library in Signal Processing
Publisher
Elsevier
Published
2014
Pages
1271-1377
ISSN
2351-9819 (ISSN-L)
DOI
10.1016/b978-0-12-396502-8.00023-1
OpenAlex
W2162231089
Cited by
14
References
151
Citations by year
2025: 1 · 2023: 2 · 2021: 2 · 2020: 1 · 2018: 2 · 2017: 2 · 2016: 2 · 2015: 2
FWCI
5.93
Type
book chapter
Language
en
Open access
closed
Locations
Academic press library in signal processing (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
  • Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
Keywords
compressed sensing, sparse signal recovery, sparse representation, sparse priors

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CoChat. (2026). Citation check: Sparsity-Aware Learning and Compressed Sensing: An Overview. https://app.cochat.ai/papers/10.1016/b978-0-12-396502-8.00023-1
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Theodoridis, S., Kopsinis, Y., & Slavakis, K. (2014). Sparsity-Aware Learning and Compressed Sensing: An Overview. Academic Press Library in Signal Processing. https://doi.org/10.1016/b978-0-12-396502-8.00023-1

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.